Global Longitudinal Strain Compared to Left Ventricular Ejection Fraction for Early Identification of Subclinical Cardiotoxicity in Cancer Patients: A Systematic Review

Global Longitudinal Strain Compared to Left Ventricular Ejection Fraction for Early Identification of Subclinical Cardiotoxicity in Cancer Patients: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Global Longitudinal Strain Compared to Left Ventricular Ejection Fraction for Early Identification of Subclinical Cardiotoxicity in Cancer Patients: A Systematic Review Moontasir Ahmed, Jannatara Tina, Shadman Newaz, Rashid Shahriar Sazal, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9242876/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer therapies, while life-saving, can cause cancer therapy-related cardiac dysfunction (CTRCD). Left ventricular ejection fraction (LVEF) has limitations in detecting subclinical myocardial injury. This systematic review synthesizes the current evidence on the diagnostic and prognostic utility of global longitudinal strain (GLS), derived from speckle-tracking echocardiography, compared to LVEF for the early identification of subclinical cardiotoxicity. Methods We systematically searched PubMed and Science Direct from inception to January, 2026 for studies comparing GLS to LVEF for detecting CTRCD in cancer patients. Data on study characteristics, patient demographics, cancer and therapy types, diagnostic accuracy, and prognostic value were extracted. The risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. Results 47 studies were included. The evidence consistently demonstrates the superior sensitivity of GLS over LVEF for the early detection of subclinical CTRCD. GLS changes often preceded a significant LVEF decline by weeks to months. A preserved GLS had a high negative predictive value (often > 90–95%) for ruling out future LVEF-defined cardiotoxicity. Furthermore, abnormal GLS was associated with a higher risk of subsequent clinical heart failure events. One randomized controlled trial (the SUCCOUR trial) showed that a GLS-guided management strategy reduced the incidence of CTRCD compared to an LVEF-guided strategy. Conclusion GLS is a more sensitive and prognostically valuable tool than LVEF for the early identification of subclinical cardiotoxicity. Its integration into routine surveillance protocols for patients receiving cardiotoxic cancer therapy is strongly supported by the evidence. A GLS-guided approach for initiating cardioprotective therapy should be considered to improve cardiac outcomes in oncology patients. Global Longitudinal Strain GLS Left Ventricular Ejection Fraction LVEF Cardiotoxicity Cancer Therapy-Related Cardiac Dysfunction CTRCD Speckle-Tracking Echocardiography Systematic Review Figures Figure 1 Figure 2 1. Introduction Advances in cancer treatment have significantly improved survival rates, shifting clinical focus towards managing long-term complications. Among these, cardiovascular disease represents a major cause of morbidity and mortality in cancer patients and survivors. Cancer therapy-related cardiac dysfunction (CTRCD), particularly from anthracyclines and HER2-targeted therapies, is a well-recognized complication that can limit treatment efficacy and impact quality of life ( 1 , 2 ). Left ventricular ejection fraction (LVEF) measured by echocardiography has traditionally been the cornerstone for monitoring cardiac function during and after cancer therapy. However, LVEF has significant limitations, including load dependency, high inter-observer variability, and, most critically, low sensitivity for detecting subclinical myocardial injury. A clinically significant drop in LVEF often represents a relatively late stage of myocardial damage, by which point functional recovery may be less achievable ( 3 , 4 ). Global longitudinal strain (GLS), measured by two-dimensional speckle-tracking echocardiography (2D-STE), provides a quantitative, more reproducible, and highly sensitive assessment of myocardial deformation. There is growing evidence that a reduction in GLS occurs early in the cardiotoxic process, often preceding a detectable fall in LVEF, thereby identifying patients at risk for overt cardiomyopathy at a potentially reversible stage ( 5 , 6 ). Over the past decade, a substantial body of evidence has characterized the utility of GLS in oncologic populations. However, a comprehensive synthesis comparing its performance directly against the standard of LVEF is needed to consolidate our understanding and inform clinical guidelines. This systematic review aims to provide a detailed analysis of the diagnostic accuracy, prognostic value, and clinical impact of GLS compared to LVEF for the early identification of subclinical CTRCD across a wide range of cancer patients and treatments. 2. Methods This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 2.1. Search Strategy and Selection Criteria A systematic search was performed in PubMed and Science Direct from database inception to January, 2026. The search strategy combined terms related to ("global longitudinal strain" OR "myocardial strain" OR "speckle tracking") AND ("cardiotoxicity" OR "cancer therapy-related cardiac dysfunction") AND ("ejection fraction" OR "LVEF") AND ("cancer" OR "neoplasm" OR "oncology"). Studies were included if they: ( 1 ) compared GLS to LVEF for detecting CTRCD in human cancer patients; ( 2 ) provided original data on diagnostic accuracy, prognostic value, or clinical outcomes; and ( 3 ) were published in English. Cohort studies, case-control studies, randomized controlled trials, and registries were eligible. 2.2. Data Extraction and Quality Assessment Two reviewers independently screened titles, abstracts, and full-text articles. Data were extracted using a standardized form, capturing information on study design, patient demographics, cancer types, treatments, imaging modalities, GLS and LVEF outcomes, and key findings. The risk of bias for RCTs was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. 2.3. Data Synthesis Given the heterogeneity in study designs, populations, and definitions of cardiotoxicity, a narrative synthesis was conducted. Data are presented in summary tables and descriptive text. 3. Results 3.1. Study Selection and Characteristics The initial search yielded 241 records. After removing duplicates and screening titles and abstracts, 69 full-text articles were assessed for eligibility. Ultimately, 47 studies were included in the final synthesis (Fig. 1 ). 3.2. Risk of Bias Assessment The methodological quality of the included studies was assessed. The overall risk of bias was low to moderate. Common limitations included the retrospective nature of some studies, small sample sizes, and single-center designs. The risk of bias summary and graph are presented in Figs. 2 a and 2 b. 3.3. Geographical Distribution and Research Output The 47 included studies originated from a range of countries, with the United States (n = 7), Canada (n = 5), Japan (n = 5), and China (n = 5) being the largest contributors (Table 1 ). The presence of multi-national collaborations (n = 6) strengthened the generalizability of findings. This distribution highlights the global recognition of cardiotoxicity as a critical issue in oncology. Table 1 Geographic Distribution of Included Studies (n = 47) Country / Region Number of Studies References (Study Numbers) USA 7 13, 34, 39, 41, 43, 45, 46 Canada 5 3, 6, 8, 32, 46 Japan 5 2, 7, 12, 23, 28 China 5 15, 20, 22, 26, 36 The Netherlands 4 1, 10, 11, 24 Italy 4 21, 25, 38, 44 Australia 3 14, 40, 42 Brazil 2 18, 29 Egypt 1 17 India 1 33 Iran 1 16 Lithuania 1 35 Nigeria 1 37 Norway 1 47 Portugal 1 5 Romania 1 19 Spain 1 27 Türkiye 1 30 United Kingdom 1 4 Multinational 6 6, 9, 14, 26, 34, 39 The 47 studies included in this review were conducted across 19 different countries, with a significant concentration in North America, Europe, and East Asia. The United States was the most represented single country (n = 7), followed by Canada (n = 5), Japan (n = 5), and China (n = 5). This distribution highlights the global recognition of cardiotoxicity as a critical issue in oncology. Six studies were multinational, collaborative efforts ( 6 , 9 , 14 , 26 , 34 , 39 ), which often provide more generalizable results due to diverse patient populations and clinical practices. The presence of studies from a wide range of economic settings, including Nigeria ( 37 ), Iran ( 16 ), and India ( 33 ), indicates that the challenge of cancer therapy-related cardiac dysfunction (CTRCD) is a worldwide concern, though the resources and protocols for surveillance may vary. 3.4. Study Design, Patient Population, and Cancer Types The evidence base was composed predominantly of prospective and retrospective cohort studies (n = 27), reflecting the observational nature of most research in this field (Table 2 ). The single Randomized Controlled Trial (RCT) by Thavendiranathan et al. (the SUCCOUR trial) ( 6 ) provides the highest level of evidence for a GLS-guided management strategy. Sample sizes varied widely, from 20 to over 1,800 patients. Breast cancer was the most extensively studied malignancy (26 studies), followed by lymphomas and leukemias (12 studies), reflecting the high usage of known cardiotoxic agents in these cancers. Table 2 Study Design and Patient Population Characteristics Characteristic Summary Description References (Examples) Study Designs Prospective Cohort ( 17 ), Retrospective Cohort ( 10 ), Cross-sectional ( 7 ), RCT ( 1 ), Case-Control ( 4 ), Observational ( 5 ), Study Protocol ( 2 ) RCT: ( 6 ); Prospective: ( 3 , 5 , 8 , 11 , 16 , 17 , 27 – 29 , 32 , 35 – 40 , 42 , 46 ) Total Sample Size Ranged from 20 to 1,829 patients. Several large cohort studies (> 500 pts): ( 10 ) (n = 1,674), ( 13 ) (n = 1,820), ( 28 ) (n = 383). ( 10 , 13 , 28 ) Cancer Types Breast Cancer (26 studies), Lymphoma/Leukemia (12 studies), Mixed/Other (Melanoma, Lung, Pediatric Sarcomas, etc.) (9 studies). Breast: ( 3 , 5 , 6 , 8 , 12 , 15 , 18 , 27 , 29 , 32 , 35 – 40 , 42 , 43 , 45 , 46 ); Lymphoma/Leukemia: ( 1 , 7 , 10 , 11 , 13 , 16 , 19 , 21 , 24 , 25 , 41 , 47 ) Patient Demographics Predominantly female (reflecting breast cancer focus). Mean/median age ranged from pediatric ( 7 , 11 , 19 , 20 , 21 , 24 , 44 ) to adult populations (~ 50–65 years). Pediatric: ( 11 , 19 , 20 ); Adult: ( 2 – 6 ) The evidence base is composed predominantly of prospective and retrospective cohort studies (n = 27), reflecting the observational nature of most research in this field. The single Randomized Controlled Trial (RCT) by Thavendiranathan et al. (the SUCCOUR trial) ( 6 ) provides the highest level of evidence for a GLS-guided management strategy. Sample sizes varied widely, with a few very large studies providing robust prevalence data ( 10 , 13 ). Breast cancer was the most extensively studied malignancy (26 studies), followed by lymphomas and leukemias (12 studies). This focus is likely due to the high usage of known cardiotoxic agents like anthracyclines and trastuzumab in these cancers. The study populations included both adults and long-term survivors of childhood cancers, indicating concern for cardiotoxicity across the age spectrum. 3.5. Chemotherapy Regimens and Cardiotoxicity Definitions Anthracyclines were the most frequently studied cardiotoxic agent, featured in 32 studies, often in combination with trastuzumab (Table 3 ). The definition of cardiotoxicity was not uniform, though most studies used a version of the common consensus criteria: a significant drop in LVEF (e.g., ≥ 10% to a value 15%). The timing of detection varied, with most studies focusing on early, subclinical dysfunction during active treatment. Table 3 Chemotherapy Regimens and Cardiotoxicity Definitions Characteristic Summary Description References (Examples) Primary Chemotherapy Anthracyclines (32 studies), Trastuzumab/Anti-HER2 (16 studies), Immune Checkpoint Inhibitors (ICIs) (3 studies), BRAF/MEK inhibitors (1 study). Anthracyclines: ( 1 , 3 – 5 , 7 , 10 , 11 , 13 , 16 – 22 , 24 , 27 – 29 , 35 – 37 , 39 , 41 , 43 , 44 , 47 ); Trastuzumab: ( 3 , 6 , 8 , 12 , 17 , 18 , 23 , 27 , 32 , 39 , 43 , 45 , 46 ); ICIs: ( 2 , 30 , 34 ) Cardiotoxicity Definition LVEF-based: Most common: ≥10% absolute drop to a value < 53% or 15% relative reduction from baseline. Some used > 12% relative reduction. LVEF drop: ( 5 , 6 , 8 , 12 , 16 , 18 , 27 – 29 , 32 , 35 , 43 ); GLS drop: ( 4 , 6 , 8 , 9 , 18 , 23 , 29 , 38 ) Timing of Detection Early/Subclinical (during or shortly after therapy): 32 studies. Late/Subclinical (long-term survivors): 10 studies. Early: ( 2 , 5 , 6 , 9 , 16 – 18 , 22 , 27 – 29 , 35 , 36 , 37 ); Late: ( 1 , 7 , 10 , 11 , 13 , 21 , 24 , 39 , 40 , 47 ) Anthracyclines were the most frequently studied cardiotoxic agent, featured in 32 studies, often in combination with trastuzumab. This aligns with their well-established dose-dependent cardiotoxic effects. There is a growing body of literature on newer agents, such as Immune Checkpoint Inhibitors (ICIs) ( 2 , 30 , 34 ) and BRAF/MEK inhibitors ( 4 ). The definition of cardiotoxicity was not uniform, though most studies used a version of the common consensus criteria: a significant drop in LVEF (e.g., ≥ 10% to a value 15%). The SUCCOUR trial ( 6 ) used a ≥ 12% relative GLS reduction to trigger cardioprotective therapy. The timing of detection varied, with most studies focusing on early, subclinical dysfunction during active treatment, while a significant number assessed the long-term cardiac sequelae in survivors many years post-therapy. 3.6. Imaging Modalities and Strain Parameters Two-dimensional speckle-tracking echocardiography (2D-STE) was the dominant imaging technique for strain assessment, used in 41 out of 47 studies (Table 4 ). Global Longitudinal Strain (GLS) was the near-universal strain parameter of interest. The widespread use of GE Healthcare’s EchoPAC software highlights a potential source of vendor-related variability in absolute GLS values. Table 4 Imaging Modalities and Strain Parameters Characteristic Summary Description References (Examples) Primary Imaging Modality 2D Speckle-Tracking Echocardiography (2D-STE) (41 studies). Cardiac Magnetic Resonance (CMR) with feature tracking (5 studies). 3D-STE (5 studies). 2D-STE: ( 2 – 6 , 9 – 12 , 15 – 20 , 22 , 23 , 27 – 30 , 33 , 35 – 43 , 45 , 47 ); CMR: ( 1 , 3 , 8 , 32 , 46 ); 3D-STE: ( 7 , 21 , 25 , 33 , 44 ) Vendor/Software GE EchoPAC was the most commonly reported software (20 + studies). Others included TomTec, Philips QLAB, and vendor-independent platforms. GE EchoPAC: ( 2 – 6 , 9 , 11 , 18 , 20 , 22 – 25 , 30 , 32 , 36 , 38 – 40 , 42 , 45 , 47 ) Strain Parameter Global Longitudinal Strain (GLS) was the primary parameter in 46 studies. A few studies also assessed GCS, GRS, and left atrial strain. GLS: All except ( 7 ); GCS/GRS: ( 1 , 7 , 10 , 11 , 21 , 24 , 26 , 32 , 33 , 36 , 42 ); LA Strain: ( 8 , 28 , 38 ) Two-dimensional speckle-tracking echocardiography (2D-STE) was the dominant imaging technique for strain assessment, used in 41 out of 47 studies, underscoring its clinical accessibility and established role. Cardiac Magnetic Resonance (CMR) with feature-tracking was used in 5 studies ( 1 , 3 , 8 , 32 , 46 ), often as a more precise reference standard for LVEF or for tissue characterization. Three-dimensional STE (3D-STE) was employed in 5 studies ( 7 , 21 , 25 , 33 , 44 ) and was noted to be potentially more sensitive than 2D-STE but sometimes limited by image quality ( 33 ). Global Longitudinal Strain (GLS) was the near-universal strain parameter of interest, validating its position as the primary recommended strain measure in cardio-oncology guidelines. The widespread use of GE Healthcare's EchoPAC software highlights a potential source of vendor-related variability in absolute GLS values. 3.7. Key Findings on Diagnostic and Prognostic Utility of GLS vs. LVEF The central theme across nearly all studies is the superior sensitivity of GLS for detecting subclinical myocardial injury compared to LVEF (Table 5 ). Multiple studies demonstrated that a relative reduction in GLS often precedes a detectable fall in LVEF by weeks to months ( 5 , 11 , 22 ). This early detection is coupled with a high Negative Predictive Value (NPV); a stable GLS value provides strong reassurance that significant LVEF drop is unlikely in the near future ( 5 , 16 ). Beyond diagnosis, GLS has prognostic significance. Pre-chemotherapy GLS was an independent predictor of future cardiac events ( 41 ), and a GLS-guided management strategy in the SUCCOUR trial led to a significant reduction in cardiotoxicity rates ( 6 ). Table 5 Key Findings on Diagnostic and Prognostic Utility of GLS vs. LVEF Finding Category Summary Description References (Examples) Superior Sensitivity of GLS GLS detected subclinical dysfunction earlier than LVEF in 25 + studies. GLS changes often occurred before any significant LVEF decline. ( 5 , 11 , 15 , 16 , 18 , 22 , 24 , 27 , 33 , 35 – 37 , 46 ) High Negative Predictive Value (NPV) Preserved GLS had a high NPV (often > 90–95%) for ruling out future LVEF-defined cardiotoxicity. ( 5 , 16 , 17 , 41 ) Prognostic Value for Clinical Events Abnormal GLS (pre- or early post-chemotherapy) was associated with a higher risk of subsequent clinical heart failure and major adverse cardiac events. ( 6 , 28 , 34 , 41 , 43 ) GLS-Guided Management The SUCCOUR RCT ( 6 ) showed that a GLS-guided strategy for initiating cardioprotective therapy reduced the incidence of CTRCD compared to an EF-guided strategy. ( 6 ) Complementary Role in Long-Term Survivors In survivors with preserved LVEF, abnormal GLS was common (e.g., 24–32%) and identified a high-risk subgroup missed by LVEF alone. ( 10 , 13 , 21 , 47 ) The central theme across nearly all studies is the superior sensitivity of GLS for detecting subclinical myocardial injury compared to LVEF. Multiple studies demonstrated that a relative reduction in GLS often precedes a detectable fall in LVEF by weeks to months ( 5 , 11 , 22 ). This early detection is coupled with a high Negative Predictive Value (NPV); a stable GLS value provides strong reassurance that significant LVEF drop is unlikely in the near future ( 5 , 16 ). Beyond diagnosis, GLS has prognostic significance. Pre-chemotherapy GLS was an independent predictor of future cardiac events in a large retrospective study ( 41 ), and a GLS-guided management strategy in the SUCCOUR trial led to a significant reduction in cardiotoxicity rates ( 6 ). In long-term survivors, studies consistently found a high prevalence of impaired GLS despite normal LVEF, uncovering a significant burden of subclinical disease ( 10 , 13 , 47 ). 3.8. Diagnostic Accuracy of GLS for Predicting LVEF-Defined Cardiotoxicity The diagnostic performance of GLS for predicting subsequent LVEF-defined cardiotoxicity varied across studies (Table 6 ). Sensitivity was generally high, ranging from 65% to 100% ( 16 – 18 , 27 , 35 , 41 , 43 ). The consistently high Negative Predictive Value (NPV), often exceeding 90–95% ( 5 , 16 , 17 , 41 ), is a clinically crucial finding. The Positive Predictive Value (PPV) was generally lower (e.g., 23–60%) ( 17 , 41 , 43 ). Table 6 Diagnostic Accuracy Metrics of GLS for Predicting LVEF-Defined Cardiotoxicity Study Sensitivity (%) Specificity (%) PPV (%) NPV (%) Cut-off Used for GLS 5 90 45 28 95 Worsening GLS (associated with 4.9x risk) 16 100 85.1 - - GLS > -17% at 4 weeks 17 69.2 80.3 60 86 GLS < -18.6% at 3 months 18 80–100 93–99 - - 14% relative reduction or GLS < -16.6% at 3 months 27 86 (GLS) 71 (GLS) - - GLS < 20.3% at 1 month post-anthracycline 35 72.7 92.1 - - GLS ≤ -18.0% 36 84.4 (MCI) 90.4 (MCI) - - Myocardial Composite Index (GLS x LV Twist) 41 86 81 23 99 Pre-chemo GLS 15% The diagnostic performance of GLS for predicting subsequent LVEF-defined cardiotoxicity varied across studies, reflecting differences in populations, timing, and cut-offs used. Sensitivity was generally high, ranging from 65% to 100% ( 16 – 18 , 27 , 35 , 41 , 43 ), meaning GLS successfully identifies most patients who will go on to develop cardiotoxicity. Specificity was more variable (45% to 99%) ( 5 , 17 , 35 , 41 ), indicating that a positive GLS signal can sometimes occur in patients who would not have developed an LVEF drop. The consistently high Negative Predictive Value (NPV), often exceeding 90–95% ( 5 , 16 , 17 , 41 ), is a clinically crucial finding. It means that if a patient's GLS remains stable, the clinician can be highly confident that they are not at immediate risk for significant LVEF decline. The Positive Predictive Value (PPV) was generally lower (e.g., 23–60%) ( 17 , 41 , 43 ), highlighting that not all patients with GLS worsening will develop overt cardiotoxicity, but they may still represent a group with subclinical injury warranting closer observation. 3.9. Prevalence of Cardiotoxicity and Key Strain Changes The prevalence of GLS-defined dysfunction is consistently and significantly higher than LVEF-defined CTRCD across all therapy types (Table 7 ), underscoring GLS's ability to detect a larger spectrum of subclinical injury. For anthracyclines, GLS decline is very common. In trastuzumab therapy, GLS changes are often reversible. In radiotherapy and long-term survivors, GLS reveals a significant burden of disease that is completely invisible to LVEF assessment. Table 7 Summary of Novel Imaging Parameters and Approaches Parameter / Approach Description Key Findings / Utility References Myocardial Work Load-independent parameter combining GLS and non-invasive blood pressure. Added diagnostic value over GLS in patients with significant BP changes. ( 3 , 25 ) Left Atrial Strain Measure of atrial reservoir (PALS), conduit, and contractile function. Early changes associated with subsequent CTRCD; provides incremental value to GLS. ( 8 , 28 , 38 ) 3D Strain Volumetric assessment of strain (GLS, GCS, GRS, GAS). More sensitive than 2D-STE for detecting small changes in LV function. ( 7 , 21 , 33 ) CMR Feature-Tracking Strain analysis from standard CMR cine images. Detected subtle, reversible dysfunction during therapy; correlated with LVEF changes. ( 1 , 32 , 46 ) Regional Strain Assessment of strain in specific myocardial segments (e.g., basal, septal). May provide early warning, e.g., basal LS in ICI myocarditis ( 2 ). ( 2 , 5 , 16 ) Beyond GLS, several advanced parameters show promise. Myocardial work indices account for afterload, potentially refining diagnosis in patients with variable blood pressure ( 3 , 25 ). Left atrial strain, a marker of diastolic function and LV filling pressure, was independently associated with future CTRCD in multiple studies ( 8 , 28 , 38 ), suggesting a role for broader echocardiographic assessment. Three-dimensional strain and CMR-based feature-tracking represent more advanced, potentially more sensitive techniques, though they may be limited by availability and expertise ( 7 , 33 , 46 ). Some studies also suggested that specific regional strain patterns, such as impairment in the septal and anterior walls ( 5 ) or a reduction in basal longitudinal strain ( 2 ), could be particularly sensitive early markers of specific types of cardiotoxicity. 3.10. Risk Factors for Cardiotoxicity The identified risk factors for CTRCD are multifactorial (Table 8 ). Treatment-related factors, especially cumulative anthracycline dose and combination therapies, are strongly implicated. Traditional cardiovascular risk factors (hypertension, diabetes) are consistently important. A key finding is that a lower (less negative) baseline GLS itself is a powerful risk factor ( 18 , 41 , 43 ). Table 8 Prevalence of Cardiotoxicity and Key Strain Changes Across Major Cancer Therapy Types Cancer Therapy Category Studies (n) LVEF-Defined CTRCD Prevalence (Range) GLS-Defined Dysfunction Prevalence (Range) Key Strain Findings Summary Anthracyclines (± Trastuzumab) 25 0% − 27.9% ( 29 , 35 ) 14% − 62.9% ( 29 , 36 ) GLS decline is common and often precedes LVEF drop. High NPV of preserved GLS. ( 5 , 16 , 35 , 37 ) Trastuzumab-Based 10 2.1% − 28.4% ( 12 , 23 ) Worsening GLS: 28.4% − 42% ( 8 , 23 ) GLS changes are often reversible upon therapy cessation. ( 23 , 46 ) Immune Checkpoint Inhibitors (ICIs) 3 Myocarditis: 2.2% − 4.7% ( 2 , 30 ) GLS reduction (≥ 15%): 9% ( 30 ) GLS is severely reduced during ICI-myocarditis and predicts MACE, even with preserved LVEF. ( 2 , 34 ) Radiotherapy (Breast/Thoracic) 5 0% − 2.1% ( 26 , 31 , 42 ) GLS reduction (≥ 10%): 32% − 44% ( 31 , 40 ) GLS detects persistent subclinical dysfunction post-RT despite unchanged LVEF. ( 31 , 40 , 42 ) Childhood Cancer Survivors (Late Effects) 8 Abnormal LVEF: 5.8% − 24.2% ( 10 , 13 ) Abnormal GLS: 28% − 54% ( 10 , 11 , 13 , 47 ) GLS uncovers a high burden of subclinical systolic dysfunction missed by LVEF alone. ( 10 , 13 , 21 , 47 ) This table synthesizes data on the prevalence of cardiotoxicity, highlighting the stark contrast between the sensitivity of LVEF and GLS. The prevalence of GLS-defined dysfunction is consistently and significantly higher than LVEF-defined CTRCD across all therapy types, underscoring GLS's ability to detect a larger spectrum of subclinical injury. For anthracyclines, GLS decline is very common. In trastuzumab therapy, GLS changes are often reversible. For ICIs, GLS plays a critical role in risk-stratifying patients with myocarditis. In radiotherapy and long-term survivors, GLS reveals a significant burden of disease that is completely invisible to LVEF assessment. This comparative overview strongly argues for the use of GLS to understand the true scope of cancer therapy-related cardiac injury. 3.11. Novel Imaging Parameters and Methodological Considerations Beyond GLS, several advanced parameters show promise, including myocardial work, left atrial strain, and 3D strain (Table 9 ). Interpreting the collective evidence requires consideration of methodological constraints (Table 10 ). The most frequent limitations are small sample sizes and single-center designs, which affect the robustness and generalizability of the findings. The use of vendor-specific software for strain analysis introduces measurement variability. Table 9 Risk Factors for Cardiotoxicity Identified in the Included Studies Risk Factor Category Specific Risk Factors Supporting References Treatment-Related • High cumulative anthracycline dose ( 10 , 13 , 16 , 19 , 41 ) • Chest/Mediastinal Radiotherapy dose ( 1 , 10 , 13 , 40 , 42 , 47 ) • Combination therapy (e.g., anthracycline + trastuzumab) ( 5 , 39 , 43 ) • Trastuzumab use ( 5 , 43 ) ( 1 , 5 , 10 , 13 , 16 , 19 , 39 , 40 – 43 , 47 ) Patient-Related (Demographics) • Older age ( 22 , 29 ) • Female sex (for radiotherapy effects) ( 10 ) • Younger age at treatment (for late effects in survivors) ( 10 , 22 , 29 ) Patient-Related (Cardiovascular Comorbidities) • Hypertension ( 17 , 29 , 35 , 43 ) • Diabetes Mellitus ( 1 , 35 ) • Preexisting Cardiovascular Disease ( 28 , 41 , 43 ) • Renal Failure ( 43 ) ( 1 , 17 , 28 , 29 , 35 , 41 , 43 ) Baseline Cardiac Parameters • Lower baseline GLS ( 18 , 41 , 43 ) • Lower baseline LVEF ( 41 , 43 ) • Larger baseline LV dimensions ( 4 ) ( 4 , 18 , 41 , 43 ) Biomarkers • Elevated baseline or on-treatment Troponin ( 16 , 28 ) • Elevated on-treatment NT-proBNP ( 17 , 28 , 35 ) ( 16 , 17 , 28 , 35 ) The identified risk factors for CTRCD are multifactorial, spanning treatment, patient, and cardiac characteristics. This table provides a structured overview crucial for risk stratification. Treatment-related factors, especially cumulative dose and combination therapies, are strongly implicated. Traditional cardiovascular risk factors (hypertension, diabetes) are consistently important, emphasizing the role of pre-existing cardiovascular health. A key finding from several studies is that a lower (less negative) baseline GLS itself is a powerful risk factor, suggesting that pre-chemotherapy systolic function, even within the "normal" range, can influence susceptibility ( 18 , 41 , 43 ). This supports the practice of obtaining a baseline echocardiogram with strain prior to initiating cardiotoxic therapy. The integration of biomarkers like troponin and NT-proBNP further refines risk prediction ( 28 , 35 ). Table 10 Study Limitations as Reported by the Authors Limitation Category Frequency Description & Impact Example References Small Sample Size / Low Event Rate Very Common (≈ 25 studies) Limits statistical power, generalizability, and robust assessment of diagnostic accuracy/prognosis. ( 1 , 4 , 16 – 19 , 21 , 22 , 25 , 29 , 30 , 31 , 33 , 37 , 40 , 42 , 44 , 46 ) Single-Center Design Very Common (≈ 30 studies) Introduces potential for selection bias and limits the generalizability of findings to other institutions/populations. ( 1 , 2 , 4 , 7 , 11 , 12 , 15 – 18 , 20 – 22 , 25 – 31 , 33 , 35 , 37 , 40 – 46 ) Retrospective Design Common (10 studies) Prone to selection bias and unmeasured confounding; limits causal inference. ( 2 , 4 , 12 , 19 , 23 , 30 , 34 , 41 , 43 , 45 ) Lack of Standardized Timing Common Echocardiograms performed per clinical indication rather than fixed protocols introduces variability. ( 5 , 43 ) Vendor-Specific Software / Lack of Core Lab Common Introduces variability in strain measurements, affecting the universal applicability of absolute cut-off values. ( 3 , 9 , 13 , 18 , 33 , 38 , 45 ) Short Follow-Up Duration Common May only capture acute/subacute toxicity and miss late-onset cardiotoxicity or clinical events. ( 4 , 16 , 17 , 19 , 26 , 29 , 30 , 31 – 33 , 35 , 36 , 40 , 42 , 46 ) No Clinical Heart Failure Outcomes Common (for diagnostic studies) Many studies use LVEF drop as a surrogate endpoint; the link to symptomatic HF is not always established. ( 3 – 5 , 11 , 22 , 27 , 29 , 33 , 36 ) A critical appraisal of the limitations reported across the studies reveals common methodological challenges. The most frequent limitations are small sample sizes and single-center designs, which are inherent to many clinical investigations but affect the robustness and generalizability of the findings. The reliance on retrospective data in a significant number of studies is another key constraint. Furthermore, the use of vendor-specific software for strain analysis without a central core lab introduces measurement variability, suggesting that relative changes within a patient may be more reliable than absolute universal cut-offs. The short follow-up in many studies underscores that the current evidence base is stronger for early subclinical detection than for predicting long-term clinical heart failure outcomes. 4. Discussion 4.1. Summary of Evidence This systematic review of 47 studies provides a comprehensive synthesis of the evidence comparing global longitudinal strain (GLS) to left ventricular ejection fraction (LVEF) for the early identification of subclinical cardiotoxicity in cancer patients. The collective data presents a consistent and compelling picture: GLS is a significantly more sensitive tool for detecting early myocardial injury than LVEF. The paramount finding is that GLS changes often precede any detectable decline in LVEF, providing a critical window for early intervention [5, 11, 22]. The high negative predictive value of a stable GLS offers clinicians robust reassurance [5, 16, 17, 41], while its prognostic value for future clinical heart failure events underscores its clinical relevance [6, 28, 34, 41, 43]. The landmark SUCCOUR trial provides Level I evidence that a GLS-guided management strategy can reduce the incidence of cardiotoxicity [6]. 4.2. Interpretation in the Context of Existing Literature and Proposed Pathophysiology Our findings consolidate a paradigm shift in cardio-oncology, moving GLS from a research tool to a central component of patient management. The pathophysiological model supports that cancer therapies, particularly anthracyclines, cause subcellular and structural damage to cardiomyocytes, leading to impaired myocardial deformation (as measured by GLS) long before the heart's pumping capacity (as measured by LVEF) is compromised. GLS effectively detects this subclinical phase of injury [5, 33, 46]. The reversibility of GLS changes with certain therapies like trastuzumab further highlights its utility in monitoring dynamic cardiac changes during treatment and its association with a potentially more reversible functional decline rather than irreversible cell death [23, 46]. 4.3. Clinical and Research Implications: From Evidence to Action The synthesized evidence mandates a proactive approach to cardiac surveillance in oncology. Integration into Clinical Practice: The consensus strongly supports the integration of GLS into routine surveillance protocols for patients receiving cardiotoxic cancer therapy, as recommended by major cardio-oncology guidelines [3, 5, 6, 9, 12, 37]. Obtaining a baseline echocardiogram with GLS prior to therapy is crucial, as a lower baseline GLS has been identified as an independent risk factor for subsequent cardiotoxicity [18, 41, 43]. GLS-Guided Management: The SUCCOUR trial provides a framework for using a significant relative GLS reduction (e.g., >12-15%) to trigger the initiation of cardioprotective medications (e.g., ACE inhibitors, beta-blockers), a strategy proven to be superior to waiting for an LVEF drop [6, 23]. The Remaining Evidence Gaps: A critical finding is the need for larger, multicenter, prospective studies with long-term follow-up to firmly establish the link between subclinical GLS changes and hard clinical outcomes like heart failure hospitalization and cardiovascular mortality [8, 10, 13, 47]. There is also a clear need to standardize GLS measurement across different ultrasound vendors and to validate specific cut-off values in diverse populations [9, 18, 45]. Furthermore, research into novel parameters like myocardial work and left atrial strain suggests they may provide incremental prognostic value, warranting further investigation [3, 8, 25, 38]. 4.4. Limitations The conclusions of this review must be interpreted within the context of the limitations inherent in the source literature. The overwhelming reliance on observational studies precludes definitive causal inference for prognostic outcomes [2, 4, 12, 41]. Heterogeneity in the definitions of cardiotoxicity, GLS cut-off values, and timing of assessments exists across studies. The frequent single-center design and use of vendor-specific software may affect the generalizability of the results [1, 9, 18, 33]. Furthermore, many studies had limited follow-up duration, focusing on early subclinical changes rather than long-term clinical heart failure events [4, 16, 29, 36]. 4.5. Future Directions This review illuminates a clear path forward: Standardization: Efforts to standardize GLS acquisition and analysis across platforms are essential to establish universal cut-offs [9, 45]. Long-Term Outcomes Research: Large, prospective cohort studies are needed to correlate GLS changes with long-term hard clinical events [10, 13, 40, 47]. Precision Medicine: Research into the utility of novel strain parameters (e.g., myocardial work, left atrial strain) and their integration with biomarkers should be pursued to create multi-parameter risk scores [3, 8, 16, 28, 35]. Interventional Trials: Further RCTs are needed to refine the GLS-guided management strategy across different cancer types and therapies and to investigate interventions for patients with isolated GLS decline but preserved LVEF [14]. 5. Conclusion GLS is a superior, sensitive, and prognostically valuable tool compared to LVEF for the early identification of subclinical cardiotoxicity in cancer patients. Its integration into standard monitoring protocols enables the detection of myocardial injury at a potentially reversible stage. A paradigm shift towards GLS-guided, proactive management is essential to mitigate cardiovascular morbidity and preserve the overall health of cancer survivors. Future research should focus on standardization, validation of long-term outcomes, and exploration of advanced strain parameters. Declarations 6. Funding Resource This research did not receive any external funding or support from external entities. All aspects of this work were conducted independently, and there are no financial or material conflicts of interest to disclose. 7. Author's Contribution MA developed the methodology and wrote the methodology section. MA also conducted data extraction using a predesigned Excel spreadsheet, capturing key study details. Additionally, MA oversaw the entire review process and coordinated the writing of the manuscript. JT independently verified 50% of the extracted data to ensure accuracy and consistency. JT also wrote the results section, contributed to the final review of the manuscript, played a role in developing the study design, and assisted in refining the methodology section. SN contributed to refining the search strategy, participated in the full-text review process, and assisted in synthesizing the extracted data. SN also built the tables and diagrams for the manuscript and helped review the methodology section. RS independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. RS also conducted the full-text review for studies meeting the inclusion criteria and wrote the discussion section. MFR independently verified 50% of the extracted data alongside JT to enhance data accuracy. MFR also contributed to refining the study methodology and participated in manuscript revisions. KN wrote the introduction section and assisted in optimizing the search strategy. KN also played a role in screening fulltext articles and contributed to drafting and reviewing the discussion section. LA independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. LA also wrote the conclusion section and participated in discussions regarding study inclusion and exclusion criteria. MH contributed to writing the discussion section and provided critical revisions to improve clarity and coherence. MH also participated in reviewing the final manuscript to ensure consistency and accuracy. MSH played a role in the quality assessment of included studies and assisted in synthesizing the extracted data. MSH also contributed to reviewing the discussion and conclusion sections to ensure alignment with the study objectives. All authors contributed to the conception and design of the study, provided input on data interpretation, and participated in manuscript revisions. All authors approved the final version before submission. 8. Conflict of Interest No conflicts of interest were reported among the authors involved in this systematic review. References van der Velde N, Janus CPM, Bowen DJ, Hassing HC, Kardys I, van Leeuwen FE, et al. Detection of Subclinical Cardiovascular Disease by Cardiovascular Magnetic Resonance in Lymphoma Survivors. JACC CardioOncology. 2021;3(5):695–706. Tamura Y, Tamura Y, Takemura R, Yamada K, Taniguchi H, Iwasawa J, et al. Longitudinal Strain and Troponin I Elevation in Patients Undergoing Immune Checkpoint Inhibitor Therapy. JACC CardioOncology. 2022;4(5):673–85. Calvillo-Argüelles O, Thampinathan B, Somerset E, Shalmon T, Amir E, Fan CP, et al. Diagnostic and Prognostic Value of Myocardial Work Indices for Identification of Cancer Therapy-Related Cardiotoxicity. J Am Coll Cardiol Img. 2022;15(8):1361–76. Glen C, Adam S, McDowell K, Waterston A, Tan YY, Petrie MC, et al. 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Mahjoob MP, Sheikholeslami SA, Dadras M, Mansouri H, Haghi M, Naderian M, et al. Prognostic Value of Cardiac Biomarkers Assessment in Combination with Myocardial 2D Strain Echocardiography for Early Detection of Anthracycline-Related Cardiac Toxicity. Cardiovasc Hematol Disord Drug Targets. 2020;20(1):74–83. Shaaban NH, Abayazeed RM, Sobhy MA, Elsharkawy EM, Hammad BA. Role of Global Left Ventricle Longitudinal Strain and Cardiac Biomarkers in the Early Detection of Cancer Therapy-Related Dysfunction in Patients Treated With Cardiotoxic Chemotherapeutic Drugs in a Cardio-Oncology Clinic. Cureus. 2024;16(10):e71766. Gripp EA, Oliveira GE, Feijó LA, Garcia MI, Xavier SS, Sousa AS. Global Longitudinal Strain Accuracy for Cardiotoxicity Prediction in a Cohort of Breast Cancer Patients During Anthracycline and/or Trastuzumab Treatment. Arq Bras Cardiol. 2018;110(2):140–50. Stolojanu C, Ste flea R, Misescu-Olah AM, Alexandra I, Popoiu A, Doros G. Combined Utility of Speckle Tracking Echocardiography and Cardiac Biomarkers for Early Detection of Anthracycline-Induced Cardiotoxicity in Pediatric Oncology Patients. Biomedicines. 2024;12(12):2849. Hu HM, Zhang XL, Zhang WL, Huang DS, Du ZD. Detection of Subclinical Anthracyclines’ Cardiotoxicity in Children with Solid Tumor. Chin Med J. 2018;131(12):1450–6. Sofia R, Melita V, De Vita A, Ruggiero A, Romano A, Attinà G, et al. Cardiac Surveillance for Early Detection of Late Subclinical Cardiac Dysfunction in Childhood Cancer Survivors After Anthracycline Therapy. Front Oncol. 2021;11:624057. Chen W, Jiao Z, Li W, Han R. Two-dimensional speckle tracking echocardiography, a powerful method for the evaluation of anthracyclines induced left ventricular insufficiency. Med (Baltim). 2022;101(42):e31084. Yamada K, Tamura Y, Taniguchi H, Furukawa A, Iwasawa J, Yada H, et al. Usefulness of Global Longitudinal Strain-Guided Management in Preventing Human Epidermal Growth Factor Receptor 2 (HER2) Inhibitor-Induced Myocardial Damage. Circ Rep. 2022;4(11):526–32. Pourier MS, Mavinkurve-Groothuis AMC, Dull MM, Weijers G, Loonen J, Bellersen L, et al. Myocardial 2D Strain During Long-Term (> 5 Years) Follow-Up of Childhood Survivors of Acute Lymphoblastic Leukemia Treated With Anthracyclines. Am J Cardiol. 2020;127:163–8. Camilli M, Ballacci F, Lamendola P, Viscovo M, Tamburrini G, Tinti L, Torre I, Amore L, Hohaus S, Crea F, Lanza GA, Burzotta F, Minotti G, Lombardo A. Strain-derived myocardial work indices in adult cancer survivors: results from an observational study and comparison with available reference ranges. Cardio-Oncology. 2025;11:78. Zhu D, Li T, Zhuang H, Cui M. Early Detection of Cardiac Damage by Two-Dimensional Speckle Tracking Echocardiography After Thoracic Radiation Therapy: Study Protocol for a Prospective Cohort Study. Front Cardiovasc Med. 2022;8:735265. Díaz-Antón B, Madurga R, Zorita B, Wasniewski S, Moreno-Arciniegas A, López-Melgar B, et al. Early detection of anthracycline- and trastuzumab-induced cardiotoxicity: value and optimal timing of serum biomarkers and echocardiographic parameters. ESC Heart Fail. 2022;9(2):1127–37. Inoue K, Machino-Ohtsuka T, Nakazawa Y, Iida N, Sasamura R, Bando H, et al. Early Detection and Prediction of Anthracycline-Induced Cardiotoxicity — A Prospective Cohort Study. Circ J. 2024;88(5):751–9. Sampaio DPS, Silva JBM, Rassi DC, Freitas AF Jr, Rassi S. Echocardiographic strategy for early detection of cardiotoxicity of doxorubicin: a prospective observational study. Cardio-Oncology. 2022;8:17. Celebi Coskun E, Coskun A, Sahin AB, Levent F, Coban E, Koca F, et al. Left ventricular global longitudinal strain in patients treated with immune checkpoint inhibitors. Front Oncol. 2024;14:1453721. Li T, Zhuang H, Wang Y, Li J, Zhu D, Cui M. Two-dimensional speckle tracking echocardiography in evaluating radiation-induced heart damage. Asia Pac J Oncol Nurs. 2022;9(1):119–24. Houbois CP, Nolan M, Somerset E, Shalmon T, Esmaeilzadeh M, Lamacie MM, et al. Serial Cardiovascular Magnetic Resonance Strain Measurements to Identify Cardiotoxicity in Breast Cancer Comparison With Echocardiography. JACC Cardiovasc Imaging. 2021;14(5):962–74. Alam S, Chandra S, Saran M, Chaudhary G, Sharma A, Bhandhari M, et al. To study the usefulness and comparison of myocardial strain imaging by 2D and 3D echocardiography for early detection of cardiotoxicity in patients undergoing cardiotoxic chemotherapy. Indian Heart J. 2019;71(6):468–75. Awadalla M, Mahmood SS, Groarke JD, Hassan MZO, Nohria A, Rokicki A, et al. Global Longitudinal Strain and Cardiac Events in Patients With Immune Checkpoint Inhibitor-Related Myocarditis. J Am Coll Cardiol. 2020;75(5):467–78. Muckiene G, Vaitiekus D, Zaliaduonyte D, Zabiela V, Verseckaite-Costa R, Vaiciuliene D, et al. Prognostic Impact of Global Longitudinal Strain and NT-proBNP on Early Development of Cardiotoxicity in Breast Cancer Patients Treated with Anthracycline-Based Chemotherapy. Medicina. 2023;59(5):953. Zhu J, Xie S, Ji H, Gu X, Wu J. Evaluation of anthracycline-induced subclinical LV dysfunction by using myocardial composite index and two-dimension speckle tracking echocardiography technique. Front Cardiovasc Med. 2022;9:936212. Orimolade OA, Ogah OS, Adebiyi A, Aje A, Adebayo OM, Oguntade A, et al. Early Detection of Anthracycline-Induced Cardiotoxicity in Female Breast Cancer Patients Using Speckle Tracking Echocardiography in an African Tertiary Institution. West Afr J Med. 2024;41(12):1174–81. Di Lisi D, Moreo A, Casavecchia G, Cadeddu Dessalvi C, Bergamini C, Zito C, et al. Atrial Strain Assessment for the Early Detection of Cancer Therapy-Related Cardiac Dysfunction in Breast Cancer Women (The STRANO STUDY: Atrial Strain in Cardio-Oncology). J Clin Med. 2023;12(22):7127. Tan TC, Bouras S, Sawaya H, Sebag IA, Cohen V, Picard MH, et al. Time Trends of Left Ventricular Ejection Fraction and Myocardial Deformation Indices in a Cohort of Women with Breast Cancer Treated with Anthracyclines, Taxanes, and Trastuzumab. J Am Soc Echocardiogr. 2015;28(5):509–14. Trivedi SJ, Choudhary P, Lo Q, Sritharan HP, Iyer A, Batumalai V et al. Persistent reduction in global longitudinal strain in the longer term after radiation therapy in patients with breast cancer. Radiother Oncol. 2018. (Epub ahead of print). Ali MT, Yucel E, Bouras S, Wang L, Fei H-w, Halpern EF, et al. Myocardial Strain Is Associated with Adverse Clinical Cardiac Events in Patients Treated with Anthracyclines. J Am Soc Echocardiogr. 2016;29(6):509–14. Lo Q, Hee L, Batumalai V, Allman C, MacDonald P, Delaney GP, et al. Subclinical Cardiac Dysfunction Detected by Strain Imaging During Breast Irradiation With Persistent Changes 6 Weeks After Treatment. Int J Radiat Oncol Biol Phys. 2015;92(2):268–76. Milks MW, Velez MR, Mehta N, Ishola A, Van Houten T, Yildiz VO, et al. Usefulness of Integrating Heart Failure Risk Factors Into Impairment of Global Longitudinal Strain to Predict Anthracycline-Related Cardiac Dysfunction. Am J Cardiol. 2018;121(7):867–73. Filomena D, Versacci P, Cimino S, Mattiucci C, Maestrini V, Cantisani D et al. Echocardiographic long-term follow-up of adult survivors of pediatric cancer treated with Dexrazoxane-Anthracyclines association. Int J Cardiol. 2019; (Epub ahead of print). Koneru S, Collier P, Goldberg A, Sanghi V, Grimm R, Rodriguez L et al. Temporal Variability of Global Longitudinal Strain in Stable Patients Undergoing Chemotherapy With Trastuzumab. Am J Cardiol. 2016; (Epub ahead of print). Ong G, Brezden-Masley C, Dhir V, Deva DP, Chan KKW, Chow CM et al. Myocardial strain imaging by cardiac magnetic resonance for detection of subclinical myocardial dysfunction in breast cancer patients receiving trastuzumab and chemotherapy. Int J Cardiol. 2018; (Epub ahead of print). Christiansen JR, Massey R, Dalen H, Kanellopoulos A, Hamre H, Fosså SD, et al. Utility of Global Longitudinal Strain by Echocardiography to Detect Left Ventricular Dysfunction in Long-Term Adult Survivors of Childhood Lymphoma and Acute Lymphoblastic Leukemia. Am J Cardiol. 2016;118(3):446–52. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9242876","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":613203680,"identity":"4be98081-f5b1-43f7-87e6-d285c6b46a66","order_by":0,"name":"Moontasir Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBADHgb25oMPQAw+otQfAGnhOZZsANLCRqwWBgaJHDMJEE1Qi25787HHH2rqZMwbEswqv+bYybAxMD98dAOPFrMzx9INDhw7zCNz4EDabdltyUCHsRkb5+DTcgPongNsB3gkGBuO3ZbcxgzUwsMmTVjLvzoeCWbGtmLJbfVEajnYxswjwcbMxvhx22EitJw5liZxtu8wjwQPG7M047bjQIqQX443H5Oo+FZnLyH//uPHn9uq7fnZmx8+xqcFBTDzgElilYMA4w9SVI+CUTAKRsGIAQCgo0RknPA0FgAAAABJRU5ErkJggg==","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":true,"prefix":"","firstName":"Moontasir","middleName":"","lastName":"Ahmed","suffix":""},{"id":613203681,"identity":"4f4c8e4e-c8b3-457f-a4bf-857be70a7d63","order_by":1,"name":"Jannatara Tina","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jannatara","middleName":"","lastName":"Tina","suffix":""},{"id":613203682,"identity":"f319c603-05bd-4ded-9116-bd7d2066e374","order_by":2,"name":"Shadman Newaz","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shadman","middleName":"","lastName":"Newaz","suffix":""},{"id":613203683,"identity":"3ef46d26-db1e-4c1b-9751-60e0517ab1b3","order_by":3,"name":"Rashid Shahriar Sazal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rashid","middleName":"Shahriar","lastName":"Sazal","suffix":""},{"id":613203684,"identity":"d0bd2a90-c74a-4eb8-8456-8a51dc3cbe8b","order_by":4,"name":"M. F. Rabbi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"F.","lastName":"Rabbi","suffix":""},{"id":613203685,"identity":"941ffe6d-8d0a-4cf1-a62e-6f0559f56d27","order_by":5,"name":"Lamia Ashraf","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lamia","middleName":"","lastName":"Ashraf","suffix":""},{"id":613203686,"identity":"596031b8-930a-4e9d-b2bd-5b8707a90295","order_by":6,"name":"Kumari Preity Rani Neogie","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kumari","middleName":"Preity Rani","lastName":"Neogie","suffix":""},{"id":613203687,"identity":"10ef5425-6632-47ce-aa35-fb8715eb7609","order_by":7,"name":"Md Hasanuzzaman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"","lastName":"Hasanuzzaman","suffix":""},{"id":613203688,"identity":"e359d196-779f-4749-bf30-db35745fcd16","order_by":8,"name":"Mst Samanta Hoque","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mst","middleName":"Samanta","lastName":"Hoque","suffix":""}],"badges":[],"createdAt":"2026-03-27 09:25:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9242876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9242876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105878731,"identity":"9bf7745c-ed42-4073-91a1-34904e305411","added_by":"auto","created_at":"2026-04-01 06:21:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flow Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9242876/v1/a5d631eb782adc16cb30cf25.png"},{"id":105904537,"identity":"44b893cb-1507-4655-a760-cc0d0ef9e500","added_by":"auto","created_at":"2026-04-01 10:09:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea: Risk of Bias Summary plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb: Risk of Bias Graph\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias assessment across included studies. Figure 2a shows the proportion of studies assessed for various domains of bias, including: selection of participants, confounding variables, measurement of exposure, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting. Each domain is color-coded to represent the assessed level of bias: Low risk (green), Unclear risk (yellow), High risk (red), Critical risk (dark red), and No information (blue). Figure 2b provides a study-wise breakdown of risk of bias assessments, allowing a granular comparison across individual studies.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9242876/v1/e4f85dd06a61be3a23704e81.png"},{"id":108005755,"identity":"5d4ee33a-cc46-43bc-8446-22430ee9c596","added_by":"auto","created_at":"2026-04-28 12:47:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":825508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9242876/v1/90083450-0792-46a7-bc55-2cddd569baae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGlobal Longitudinal Strain Compared to Left Ventricular Ejection Fraction for Early Identification of Subclinical Cardiotoxicity in Cancer Patients: A Systematic Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdvances in cancer treatment have significantly improved survival rates, shifting clinical focus towards managing long-term complications. Among these, cardiovascular disease represents a major cause of morbidity and mortality in cancer patients and survivors. Cancer therapy-related cardiac dysfunction (CTRCD), particularly from anthracyclines and HER2-targeted therapies, is a well-recognized complication that can limit treatment efficacy and impact quality of life (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeft ventricular ejection fraction (LVEF) measured by echocardiography has traditionally been the cornerstone for monitoring cardiac function during and after cancer therapy. However, LVEF has significant limitations, including load dependency, high inter-observer variability, and, most critically, low sensitivity for detecting subclinical myocardial injury. A clinically significant drop in LVEF often represents a relatively late stage of myocardial damage, by which point functional recovery may be less achievable (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobal longitudinal strain (GLS), measured by two-dimensional speckle-tracking echocardiography (2D-STE), provides a quantitative, more reproducible, and highly sensitive assessment of myocardial deformation. There is growing evidence that a reduction in GLS occurs early in the cardiotoxic process, often preceding a detectable fall in LVEF, thereby identifying patients at risk for overt cardiomyopathy at a potentially reversible stage (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past decade, a substantial body of evidence has characterized the utility of GLS in oncologic populations. However, a comprehensive synthesis comparing its performance directly against the standard of LVEF is needed to consolidate our understanding and inform clinical guidelines. This systematic review aims to provide a detailed analysis of the diagnostic accuracy, prognostic value, and clinical impact of GLS compared to LVEF for the early identification of subclinical CTRCD across a wide range of cancer patients and treatments.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Search Strategy and Selection Criteria\u003c/h2\u003e \u003cp\u003eA systematic search was performed in PubMed and Science Direct from database inception to January, 2026. The search strategy combined terms related to (\"global longitudinal strain\" OR \"myocardial strain\" OR \"speckle tracking\") AND (\"cardiotoxicity\" OR \"cancer therapy-related cardiac dysfunction\") AND (\"ejection fraction\" OR \"LVEF\") AND (\"cancer\" OR \"neoplasm\" OR \"oncology\").\u003c/p\u003e \u003cp\u003eStudies were included if they: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) compared GLS to LVEF for detecting CTRCD in human cancer patients; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) provided original data on diagnostic accuracy, prognostic value, or clinical outcomes; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) were published in English. Cohort studies, case-control studies, randomized controlled trials, and registries were eligible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Extraction and Quality Assessment\u003c/h2\u003e \u003cp\u003eTwo reviewers independently screened titles, abstracts, and full-text articles. Data were extracted using a standardized form, capturing information on study design, patient demographics, cancer types, treatments, imaging modalities, GLS and LVEF outcomes, and key findings. The risk of bias for RCTs was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Synthesis\u003c/h2\u003e \u003cp\u003eGiven the heterogeneity in study designs, populations, and definitions of cardiotoxicity, a narrative synthesis was conducted. Data are presented in summary tables and descriptive text.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Selection and Characteristics\u003c/h2\u003e \u003cp\u003eThe initial search yielded 241 records. After removing duplicates and screening titles and abstracts, 69 full-text articles were assessed for eligibility. Ultimately, 47 studies were included in the final synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Risk of Bias Assessment\u003c/h2\u003e \u003cp\u003eThe methodological quality of the included studies was assessed. The overall risk of bias was low to moderate. Common limitations included the retrospective nature of some studies, small sample sizes, and single-center designs. The risk of bias summary and graph are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Geographical Distribution and Research Output\u003c/h2\u003e \u003cp\u003eThe 47 included studies originated from a range of countries, with the United States (n\u0026thinsp;=\u0026thinsp;7), Canada (n\u0026thinsp;=\u0026thinsp;5), Japan (n\u0026thinsp;=\u0026thinsp;5), and China (n\u0026thinsp;=\u0026thinsp;5) being the largest contributors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The presence of multi-national collaborations (n\u0026thinsp;=\u0026thinsp;6) strengthened the generalizability of findings. This distribution highlights the global recognition of cardiotoxicity as a critical issue in oncology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeographic Distribution of Included Studies (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry / Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13, 34, 39, 41, 43, 45, 46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 6, 8, 32, 46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 7, 12, 23, 28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15, 20, 22, 26, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 10, 11, 24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21, 25, 38, 44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14, 40, 42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18, 29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLithuania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026uuml;rkiye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultinational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6, 9, 14, 26, 34, 39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 47 studies included in this review were conducted across 19 different countries, with a significant concentration in North America, Europe, and East Asia. The United States was the most represented single country (n\u0026thinsp;=\u0026thinsp;7), followed by Canada (n\u0026thinsp;=\u0026thinsp;5), Japan (n\u0026thinsp;=\u0026thinsp;5), and China (n\u0026thinsp;=\u0026thinsp;5). This distribution highlights the global recognition of cardiotoxicity as a critical issue in oncology. Six studies were multinational, collaborative efforts (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), which often provide more generalizable results due to diverse patient populations and clinical practices. The presence of studies from a wide range of economic settings, including Nigeria (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), Iran (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and India (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), indicates that the challenge of cancer therapy-related cardiac dysfunction (CTRCD) is a worldwide concern, though the resources and protocols for surveillance may vary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Study Design, Patient Population, and Cancer Types\u003c/h2\u003e \u003cp\u003eThe evidence base was composed predominantly of prospective and retrospective cohort studies (n\u0026thinsp;=\u0026thinsp;27), reflecting the observational nature of most research in this field (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The single Randomized Controlled Trial (RCT) by Thavendiranathan et al. (the SUCCOUR trial) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) provides the highest level of evidence for a GLS-guided management strategy. Sample sizes varied widely, from 20 to over 1,800 patients. Breast cancer was the most extensively studied malignancy (26 studies), followed by lymphomas and leukemias (12 studies), reflecting the high usage of known cardiotoxic agents in these cancers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Design and Patient Population Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummary Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Examples)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Designs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProspective Cohort (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), Retrospective Cohort (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), Cross-sectional (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), RCT (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), Case-Control (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), Observational (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), Study Protocol (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCT: (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e); Prospective: (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Sample Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRanged from 20 to 1,829 patients. Several large cohort studies (\u0026gt;\u0026thinsp;500 pts): (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;1,674), (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;1,820), (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;383).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast Cancer (26 studies), Lymphoma/Leukemia (12 studies), Mixed/Other (Melanoma, Lung, Pediatric Sarcomas, etc.) (9 studies).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast: (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e); Lymphoma/Leukemia: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient Demographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredominantly female (reflecting breast cancer focus). Mean/median age ranged from pediatric (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) to adult populations (~\u0026thinsp;50\u0026ndash;65 years).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePediatric: (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e); Adult: (\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe evidence base is composed predominantly of prospective and retrospective cohort studies (n\u0026thinsp;=\u0026thinsp;27), reflecting the observational nature of most research in this field. The single Randomized Controlled Trial (RCT) by Thavendiranathan et al. (the SUCCOUR trial) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) provides the highest level of evidence for a GLS-guided management strategy. Sample sizes varied widely, with a few very large studies providing robust prevalence data (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Breast cancer was the most extensively studied malignancy (26 studies), followed by lymphomas and leukemias (12 studies). This focus is likely due to the high usage of known cardiotoxic agents like anthracyclines and trastuzumab in these cancers. The study populations included both adults and long-term survivors of childhood cancers, indicating concern for cardiotoxicity across the age spectrum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Chemotherapy Regimens and Cardiotoxicity Definitions\u003c/h2\u003e \u003cp\u003eAnthracyclines were the most frequently studied cardiotoxic agent, featured in 32 studies, often in combination with trastuzumab (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The definition of cardiotoxicity was not uniform, though most studies used a version of the common consensus criteria: a significant drop in LVEF (e.g., \u0026ge;\u0026thinsp;10% to a value\u0026thinsp;\u0026lt;\u0026thinsp;53\u0026ndash;55%) and/or a significant relative reduction in GLS (typically\u0026thinsp;\u0026gt;\u0026thinsp;15%). The timing of detection varied, with most studies focusing on early, subclinical dysfunction during active treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemotherapy Regimens and Cardiotoxicity Definitions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummary Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Examples)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnthracyclines (32 studies), Trastuzumab/Anti-HER2 (16 studies), Immune Checkpoint Inhibitors (ICIs) (3 studies), BRAF/MEK inhibitors (1 study).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnthracyclines: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e); Trastuzumab: (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e); ICIs: (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiotoxicity Definition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLVEF-based: Most common: \u0026ge;10% absolute drop to a value\u0026thinsp;\u0026lt;\u0026thinsp;53% or \u0026lt;\u0026thinsp;55%. GLS-based: Most common: \u0026gt;15% relative reduction from baseline. Some used\u0026thinsp;\u0026gt;\u0026thinsp;12% relative reduction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLVEF drop: (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e); GLS drop: (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiming of Detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly/Subclinical (during or shortly after therapy): 32 studies. Late/Subclinical (long-term survivors): 10 studies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly: (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e); Late: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnthracyclines were the most frequently studied cardiotoxic agent, featured in 32 studies, often in combination with trastuzumab. This aligns with their well-established dose-dependent cardiotoxic effects. There is a growing body of literature on newer agents, such as Immune Checkpoint Inhibitors (ICIs) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and BRAF/MEK inhibitors (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The definition of cardiotoxicity was not uniform, though most studies used a version of the common consensus criteria: a significant drop in LVEF (e.g., \u0026ge;\u0026thinsp;10% to a value\u0026thinsp;\u0026lt;\u0026thinsp;53\u0026ndash;55%) and/or a significant relative reduction in GLS (typically\u0026thinsp;\u0026gt;\u0026thinsp;15%). The SUCCOUR trial (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) used a\u0026thinsp;\u0026ge;\u0026thinsp;12% relative GLS reduction to trigger cardioprotective therapy. The timing of detection varied, with most studies focusing on early, subclinical dysfunction during active treatment, while a significant number assessed the long-term cardiac sequelae in survivors many years post-therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Imaging Modalities and Strain Parameters\u003c/h2\u003e \u003cp\u003eTwo-dimensional speckle-tracking echocardiography (2D-STE) was the dominant imaging technique for strain assessment, used in 41 out of 47 studies (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Global Longitudinal Strain (GLS) was the near-universal strain parameter of interest. The widespread use of GE Healthcare\u0026rsquo;s EchoPAC software highlights a potential source of vendor-related variability in absolute GLS values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImaging Modalities and Strain Parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummary Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Examples)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Imaging Modality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2D Speckle-Tracking Echocardiography (2D-STE) (41 studies). Cardiac Magnetic Resonance (CMR) with feature tracking (5 studies). 3D-STE (5 studies).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2D-STE: (\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40 CR41 CR42\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e); CMR: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e); 3D-STE: (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVendor/Software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGE EchoPAC was the most commonly reported software (20\u0026thinsp;+\u0026thinsp;studies). Others included TomTec, Philips QLAB, and vendor-independent platforms.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGE EchoPAC: (\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrain Parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal Longitudinal Strain (GLS) was the primary parameter in 46 studies. A few studies also assessed GCS, GRS, and left atrial strain.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLS: All except (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e); GCS/GRS: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e); LA Strain: (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-dimensional speckle-tracking echocardiography (2D-STE) was the dominant imaging technique for strain assessment, used in 41 out of 47 studies, underscoring its clinical accessibility and established role. Cardiac Magnetic Resonance (CMR) with feature-tracking was used in 5 studies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), often as a more precise reference standard for LVEF or for tissue characterization. Three-dimensional STE (3D-STE) was employed in 5 studies (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) and was noted to be potentially more sensitive than 2D-STE but sometimes limited by image quality (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Global Longitudinal Strain (GLS) was the near-universal strain parameter of interest, validating its position as the primary recommended strain measure in cardio-oncology guidelines. The widespread use of GE Healthcare's EchoPAC software highlights a potential source of vendor-related variability in absolute GLS values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Key Findings on Diagnostic and Prognostic Utility of GLS vs. LVEF\u003c/h2\u003e \u003cp\u003eThe central theme across nearly all studies is the superior sensitivity of GLS for detecting subclinical myocardial injury compared to LVEF (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Multiple studies demonstrated that a relative reduction in GLS often precedes a detectable fall in LVEF by weeks to months (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This early detection is coupled with a high Negative Predictive Value (NPV); a stable GLS value provides strong reassurance that significant LVEF drop is unlikely in the near future (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Beyond diagnosis, GLS has prognostic significance. Pre-chemotherapy GLS was an independent predictor of future cardiac events (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), and a GLS-guided management strategy in the SUCCOUR trial led to a significant reduction in cardiotoxicity rates (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Findings on Diagnostic and Prognostic Utility of GLS vs. LVEF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinding Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummary Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Examples)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Sensitivity of GLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLS detected subclinical dysfunction earlier than LVEF in 25\u0026thinsp;+\u0026thinsp;studies. GLS changes often occurred before any significant LVEF decline.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Negative Predictive Value (NPV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreserved GLS had a high NPV (often\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026ndash;95%) for ruling out future LVEF-defined cardiotoxicity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognostic Value for Clinical Events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal GLS (pre- or early post-chemotherapy) was associated with a higher risk of subsequent clinical heart failure and major adverse cardiac events.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLS-Guided Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe SUCCOUR RCT (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) showed that a GLS-guided strategy for initiating cardioprotective therapy reduced the incidence of CTRCD compared to an EF-guided strategy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplementary Role in Long-Term Survivors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn survivors with preserved LVEF, abnormal GLS was common (e.g., 24\u0026ndash;32%) and identified a high-risk subgroup missed by LVEF alone.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe central theme across nearly all studies is the superior sensitivity of GLS for detecting subclinical myocardial injury compared to LVEF. Multiple studies demonstrated that a relative reduction in GLS often precedes a detectable fall in LVEF by weeks to months (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This early detection is coupled with a high Negative Predictive Value (NPV); a stable GLS value provides strong reassurance that significant LVEF drop is unlikely in the near future (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Beyond diagnosis, GLS has prognostic significance. Pre-chemotherapy GLS was an independent predictor of future cardiac events in a large retrospective study (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), and a GLS-guided management strategy in the SUCCOUR trial led to a significant reduction in cardiotoxicity rates (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In long-term survivors, studies consistently found a high prevalence of impaired GLS despite normal LVEF, uncovering a significant burden of subclinical disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Diagnostic Accuracy of GLS for Predicting LVEF-Defined Cardiotoxicity\u003c/h2\u003e \u003cp\u003eThe diagnostic performance of GLS for predicting subsequent LVEF-defined cardiotoxicity varied across studies (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Sensitivity was generally high, ranging from 65% to 100% (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The consistently high Negative Predictive Value (NPV), often exceeding 90\u0026ndash;95% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), is a clinically crucial finding. The Positive Predictive Value (PPV) was generally lower (e.g., 23\u0026ndash;60%) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Accuracy Metrics of GLS for Predicting LVEF-Defined Cardiotoxicity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off Used for GLS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorsening GLS (associated with 4.9x risk)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLS \u0026gt; -17% at 4 weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLS \u0026lt; -18.6% at 3 months\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14% relative reduction or GLS \u0026lt; -16.6% at 3 months\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (GLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (GLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLS\u0026thinsp;\u0026lt;\u0026thinsp;20.3% at 1 month post-anthracycline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGLS \u0026le; -18.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.4 (MCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.4 (MCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMyocardial Composite Index (GLS x LV Twist)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePre-chemo GLS \u0026lt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelative GLS decline\u0026thinsp;\u0026gt;\u0026thinsp;15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic performance of GLS for predicting subsequent LVEF-defined cardiotoxicity varied across studies, reflecting differences in populations, timing, and cut-offs used. Sensitivity was generally high, ranging from 65% to 100% (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), meaning GLS successfully identifies most patients who will go on to develop cardiotoxicity. Specificity was more variable (45% to 99%) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), indicating that a positive GLS signal can sometimes occur in patients who would not have developed an LVEF drop. The consistently high Negative Predictive Value (NPV), often exceeding 90\u0026ndash;95% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), is a clinically crucial finding. It means that if a patient's GLS remains stable, the clinician can be highly confident that they are not at immediate risk for significant LVEF decline. The Positive Predictive Value (PPV) was generally lower (e.g., 23\u0026ndash;60%) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), highlighting that not all patients with GLS worsening will develop overt cardiotoxicity, but they may still represent a group with subclinical injury warranting closer observation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Prevalence of Cardiotoxicity and Key Strain Changes\u003c/h2\u003e \u003cp\u003eThe prevalence of GLS-defined dysfunction is consistently and significantly higher than LVEF-defined CTRCD across all therapy types (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), underscoring GLS's ability to detect a larger spectrum of subclinical injury. For anthracyclines, GLS decline is very common. In trastuzumab therapy, GLS changes are often reversible. In radiotherapy and long-term survivors, GLS reveals a significant burden of disease that is completely invisible to LVEF assessment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Novel Imaging Parameters and Approaches\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter / Approach\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Findings / Utility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial Work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoad-independent parameter combining GLS and non-invasive blood pressure.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdded diagnostic value over GLS in patients with significant BP changes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Atrial Strain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasure of atrial reservoir (PALS), conduit, and contractile function.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly changes associated with subsequent CTRCD; provides incremental value to GLS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3D Strain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolumetric assessment of strain (GLS, GCS, GRS, GAS).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore sensitive than 2D-STE for detecting small changes in LV function.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMR Feature-Tracking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrain analysis from standard CMR cine images.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetected subtle, reversible dysfunction during therapy; correlated with LVEF changes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional Strain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssessment of strain in specific myocardial segments (e.g., basal, septal).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay provide early warning, e.g., basal LS in ICI myocarditis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBeyond GLS, several advanced parameters show promise. Myocardial work indices account for afterload, potentially refining diagnosis in patients with variable blood pressure (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Left atrial strain, a marker of diastolic function and LV filling pressure, was independently associated with future CTRCD in multiple studies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), suggesting a role for broader echocardiographic assessment. Three-dimensional strain and CMR-based feature-tracking represent more advanced, potentially more sensitive techniques, though they may be limited by availability and expertise (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Some studies also suggested that specific regional strain patterns, such as impairment in the septal and anterior walls (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) or a reduction in basal longitudinal strain (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), could be particularly sensitive early markers of specific types of cardiotoxicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Risk Factors for Cardiotoxicity\u003c/h2\u003e \u003cp\u003eThe identified risk factors for CTRCD are multifactorial (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Treatment-related factors, especially cumulative anthracycline dose and combination therapies, are strongly implicated. Traditional cardiovascular risk factors (hypertension, diabetes) are consistently important. A key finding is that a lower (less negative) baseline GLS itself is a powerful risk factor (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of Cardiotoxicity and Key Strain Changes Across Major Cancer Therapy Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Therapy Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLVEF-Defined CTRCD Prevalence (Range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLS-Defined Dysfunction Prevalence (Range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKey Strain Findings Summary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthracyclines (\u0026plusmn;\u0026thinsp;Trastuzumab)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% \u0026minus;\u0026thinsp;27.9% (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14% \u0026minus;\u0026thinsp;62.9% (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGLS decline is common and often precedes LVEF drop. High NPV of preserved GLS. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrastuzumab-Based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1% \u0026minus;\u0026thinsp;28.4% (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorsening GLS: 28.4% \u0026minus;\u0026thinsp;42% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGLS changes are often reversible upon therapy cessation. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune Checkpoint Inhibitors (ICIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyocarditis: 2.2% \u0026minus;\u0026thinsp;4.7% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLS reduction (\u0026ge;\u0026thinsp;15%): 9% (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGLS is severely reduced during ICI-myocarditis and predicts MACE, even with preserved LVEF. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy (Breast/Thoracic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0% \u0026minus;\u0026thinsp;2.1% (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLS reduction (\u0026ge;\u0026thinsp;10%): 32% \u0026minus;\u0026thinsp;44% (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGLS detects persistent subclinical dysfunction post-RT despite unchanged LVEF. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildhood Cancer Survivors (Late Effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbnormal LVEF: 5.8% \u0026minus;\u0026thinsp;24.2% (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbnormal GLS: 28% \u0026minus;\u0026thinsp;54% (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGLS uncovers a high burden of subclinical systolic dysfunction missed by LVEF alone. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table synthesizes data on the prevalence of cardiotoxicity, highlighting the stark contrast between the sensitivity of LVEF and GLS. The prevalence of GLS-defined dysfunction is consistently and significantly higher than LVEF-defined CTRCD across all therapy types, underscoring GLS's ability to detect a larger spectrum of subclinical injury. For anthracyclines, GLS decline is very common. In trastuzumab therapy, GLS changes are often reversible. For ICIs, GLS plays a critical role in risk-stratifying patients with myocarditis. In radiotherapy and long-term survivors, GLS reveals a significant burden of disease that is completely invisible to LVEF assessment. This comparative overview strongly argues for the use of GLS to understand the true scope of cancer therapy-related cardiac injury.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.11. Novel Imaging Parameters and Methodological Considerations\u003c/h2\u003e \u003cp\u003eBeyond GLS, several advanced parameters show promise, including myocardial work, left atrial strain, and 3D strain (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Interpreting the collective evidence requires consideration of methodological constraints (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The most frequent limitations are small sample sizes and single-center designs, which affect the robustness and generalizability of the findings. The use of vendor-specific software for strain analysis introduces measurement variability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk Factors for Cardiotoxicity Identified in the Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Factor Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecific Risk Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupporting References\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; High cumulative anthracycline dose (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Chest/Mediastinal Radiotherapy dose (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Combination therapy (e.g., anthracycline\u0026thinsp;+\u0026thinsp;trastuzumab) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Trastuzumab use (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient-Related (Demographics)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Older age (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Female sex (for radiotherapy effects) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Younger age at treatment (for late effects in survivors)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient-Related (Cardiovascular Comorbidities)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Hypertension (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Diabetes Mellitus (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Preexisting Cardiovascular Disease (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Renal Failure (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Cardiac Parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Lower baseline GLS (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Lower baseline LVEF (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Larger baseline LV dimensions (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Elevated baseline or on-treatment Troponin (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Elevated on-treatment NT-proBNP (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe identified risk factors for CTRCD are multifactorial, spanning treatment, patient, and cardiac characteristics. This table provides a structured overview crucial for risk stratification. Treatment-related factors, especially cumulative dose and combination therapies, are strongly implicated. Traditional cardiovascular risk factors (hypertension, diabetes) are consistently important, emphasizing the role of pre-existing cardiovascular health. A key finding from several studies is that a lower (less negative) baseline GLS itself is a powerful risk factor, suggesting that pre-chemotherapy systolic function, even within the \"normal\" range, can influence susceptibility (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This supports the practice of obtaining a baseline echocardiogram with strain prior to initiating cardiotoxic therapy. The integration of biomarkers like troponin and NT-proBNP further refines risk prediction (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Limitations as Reported by the Authors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimitation Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription \u0026amp; Impact\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExample References\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall Sample Size / Low Event Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Common (\u0026asymp;\u0026thinsp;25 studies)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimits statistical power, generalizability, and robust assessment of diagnostic accuracy/prognosis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-Center Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Common (\u0026asymp;\u0026thinsp;30 studies)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduces potential for selection bias and limits the generalizability of findings to other institutions/populations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44 CR45\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrospective Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon (10 studies)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProne to selection bias and unmeasured confounding; limits causal inference.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Standardized Timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEchocardiograms performed per clinical indication rather than fixed protocols introduces variability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVendor-Specific Software / Lack of Core Lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduces variability in strain measurements, affecting the universal applicability of absolute cut-off values.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Follow-Up Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay only capture acute/subacute toxicity and miss late-onset cardiotoxicity or clinical events.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Clinical Heart Failure Outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon (for diagnostic studies)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMany studies use LVEF drop as a surrogate endpoint; the link to symptomatic HF is not always established.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA critical appraisal of the limitations reported across the studies reveals common methodological challenges. The most frequent limitations are small sample sizes and single-center designs, which are inherent to many clinical investigations but affect the robustness and generalizability of the findings. The reliance on retrospective data in a significant number of studies is another key constraint. Furthermore, the use of vendor-specific software for strain analysis without a central core lab introduces measurement variability, suggesting that relative changes within a patient may be more reliable than absolute universal cut-offs. The short follow-up in many studies underscores that the current evidence base is stronger for early subclinical detection than for predicting long-term clinical heart failure outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1. Summary of Evidence\u003cbr\u003e\u003c/strong\u003eThis systematic review of 47 studies provides a comprehensive synthesis of the evidence comparing global longitudinal strain (GLS) to left ventricular ejection fraction (LVEF) for the early identification of subclinical cardiotoxicity in cancer patients. The collective data presents a consistent and compelling picture: GLS is a significantly more sensitive tool for detecting early myocardial injury than LVEF. The paramount finding is that GLS changes often precede any detectable decline in LVEF, providing a critical window for early intervention\u0026nbsp;[5, 11, 22]. The high negative predictive value of a stable GLS offers clinicians robust reassurance\u0026nbsp;[5, 16, 17, 41], while its prognostic value for future clinical heart failure events underscores its clinical relevance\u0026nbsp;[6, 28, 34, 41, 43]. The landmark SUCCOUR trial provides Level I evidence that a GLS-guided management strategy can reduce the incidence of cardiotoxicity\u0026nbsp;[6].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Interpretation in the Context of Existing Literature and Proposed Pathophysiology\u003cbr\u003e\u003c/strong\u003eOur findings consolidate a paradigm shift in cardio-oncology, moving GLS from a research tool to a central component of patient management. The pathophysiological model supports that cancer therapies, particularly anthracyclines, cause subcellular and structural damage to cardiomyocytes, leading to impaired myocardial deformation (as measured by GLS) long before the heart's pumping capacity (as measured by LVEF) is compromised. GLS effectively detects this subclinical phase of injury\u0026nbsp;[5, 33, 46]. The reversibility of GLS changes with certain therapies like trastuzumab further highlights its utility in monitoring dynamic cardiac changes during treatment and its association with a potentially more reversible functional decline rather than irreversible cell death\u0026nbsp;[23, 46].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Clinical and Research Implications: From Evidence to Action\u003cbr\u003e\u003c/strong\u003eThe synthesized evidence mandates a proactive approach to cardiac surveillance in oncology.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIntegration into Clinical Practice:\u0026nbsp;The consensus strongly supports the integration of GLS into routine surveillance protocols for patients receiving cardiotoxic cancer therapy, as recommended by major cardio-oncology guidelines\u0026nbsp;[3, 5, 6, 9, 12, 37]. Obtaining a baseline echocardiogram with GLS prior to therapy is crucial, as a lower baseline GLS has been identified as an independent risk factor for subsequent cardiotoxicity\u0026nbsp;[18, 41, 43].\u003c/li\u003e\n \u003cli\u003eGLS-Guided Management:\u0026nbsp;The SUCCOUR trial provides a framework for using a significant relative GLS reduction (e.g., \u0026gt;12-15%) to trigger the initiation of cardioprotective medications (e.g., ACE inhibitors, beta-blockers), a strategy proven to be superior to waiting for an LVEF drop\u0026nbsp;[6, 23].\u003c/li\u003e\n \u003cli\u003eThe Remaining Evidence Gaps:\u0026nbsp;A critical finding is the need for larger, multicenter, prospective studies with long-term follow-up to firmly establish the link between subclinical GLS changes and hard clinical outcomes like heart failure hospitalization and cardiovascular mortality\u0026nbsp;[8, 10, 13, 47]. There is also a clear need to standardize GLS measurement across different ultrasound vendors and to validate specific cut-off values in diverse populations\u0026nbsp;[9, 18, 45]. Furthermore, research into novel parameters like myocardial work and left atrial strain suggests they may provide incremental prognostic value, warranting further investigation\u0026nbsp;[3, 8, 25, 38].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. Limitations\u003cbr\u003e\u003c/strong\u003eThe conclusions of this review must be interpreted within the context of the limitations inherent in the source literature. The overwhelming reliance on observational studies precludes definitive causal inference for prognostic outcomes\u0026nbsp;[2, 4, 12, 41]. Heterogeneity in the definitions of cardiotoxicity, GLS cut-off values, and timing of assessments exists across studies. The frequent single-center design and use of vendor-specific software may affect the generalizability of the results\u0026nbsp;[1, 9, 18, 33]. Furthermore, many studies had limited follow-up duration, focusing on early subclinical changes rather than long-term clinical heart failure events\u0026nbsp;[4, 16, 29, 36].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. Future Directions\u003cbr\u003e\u003c/strong\u003eThis review illuminates a clear path forward:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eStandardization:\u0026nbsp;Efforts to standardize GLS acquisition and analysis across platforms are essential to establish universal cut-offs\u0026nbsp;[9, 45].\u003c/li\u003e\n \u003cli\u003eLong-Term Outcomes Research:\u0026nbsp;Large, prospective cohort studies are needed to correlate GLS changes with long-term hard clinical events\u0026nbsp;[10, 13, 40, 47].\u003c/li\u003e\n \u003cli\u003ePrecision Medicine:\u0026nbsp;Research into the utility of novel strain parameters (e.g., myocardial work, left atrial strain) and their integration with biomarkers should be pursued to create multi-parameter risk scores\u0026nbsp;[3, 8, 16, 28, 35].\u003c/li\u003e\n \u003cli\u003eInterventional Trials: Further RCTs are needed to refine the GLS-guided management strategy across different cancer types and therapies and to investigate interventions for patients with isolated GLS decline but preserved LVEF [14].\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eGLS is a superior, sensitive, and prognostically valuable tool compared to LVEF for the early identification of subclinical cardiotoxicity in cancer patients. Its integration into standard monitoring protocols enables the detection of myocardial injury at a potentially reversible stage. A paradigm shift towards GLS-guided, proactive management is essential to mitigate cardiovascular morbidity and preserve the overall health of cancer survivors. Future research should focus on standardization, validation of long-term outcomes, and exploration of advanced strain parameters.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Funding Resource\u003cbr\u003e\u003c/strong\u003eThis research did not receive any external funding or support from external entities. All aspects of this work were conducted independently, and there are no financial or material conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Author's Contribution\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eMA\u003c/strong\u003e developed the methodology and wrote the methodology section. MA also conducted data extraction using a predesigned Excel spreadsheet, capturing key study details. Additionally, MA oversaw the entire review process and coordinated the writing of the manuscript. \u003cstrong\u003eJT\u003c/strong\u003e independently verified 50% of the extracted data to ensure accuracy and consistency. JT also wrote the results section, contributed to the final review of the manuscript, played a role in developing the study design, and assisted in refining the methodology section. \u003cstrong\u003eSN\u003c/strong\u003e contributed to refining the search strategy, participated in the full-text review process, and assisted in synthesizing the extracted data. SN also built the tables and diagrams for the manuscript and helped review the methodology section. \u003cstrong\u003eRS\u003c/strong\u003e independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. RS also conducted the full-text review for studies meeting the inclusion criteria and wrote the discussion section. \u003cstrong\u003eMFR\u003c/strong\u003e independently verified 50% of the extracted data alongside JT to enhance data accuracy. MFR also contributed to refining the study methodology and participated in manuscript revisions. \u003cstrong\u003eKN\u003c/strong\u003e wrote the introduction section and assisted in optimizing the search strategy. KN also played a role in screening fulltext articles and contributed to drafting and reviewing the discussion section. \u003cstrong\u003eLA\u003c/strong\u003e independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. LA also wrote the conclusion section and participated in discussions regarding study inclusion and exclusion criteria. \u003cstrong\u003eMH\u003c/strong\u003e contributed to writing the discussion section and provided critical revisions to improve clarity and coherence. MH also participated in reviewing the final manuscript to ensure consistency and accuracy. \u003cstrong\u003eMSH\u003c/strong\u003e played a role in the quality assessment of included studies and assisted in synthesizing the extracted data. MSH also contributed to reviewing the discussion and conclusion sections to ensure alignment with the study objectives. All authors contributed to the conception and design of the study, provided input on data interpretation, and participated in manuscript revisions. All authors approved the final version before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Conflict of Interest\u003cbr\u003e\u003c/strong\u003eNo conflicts of interest were reported among the authors involved in this systematic review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan der Velde N, Janus CPM, Bowen DJ, Hassing HC, Kardys I, van Leeuwen FE, et al. 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Am J Cardiol. 2016;118(3):446\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Global Longitudinal Strain, GLS, Left Ventricular Ejection Fraction, LVEF, Cardiotoxicity, Cancer Therapy-Related Cardiac Dysfunction, CTRCD, Speckle-Tracking Echocardiography, Systematic Review","lastPublishedDoi":"10.21203/rs.3.rs-9242876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9242876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer therapies, while life-saving, can cause cancer therapy-related cardiac dysfunction (CTRCD). Left ventricular ejection fraction (LVEF) has limitations in detecting subclinical myocardial injury. This systematic review synthesizes the current evidence on the diagnostic and prognostic utility of global longitudinal strain (GLS), derived from speckle-tracking echocardiography, compared to LVEF for the early identification of subclinical cardiotoxicity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe systematically searched PubMed and Science Direct from inception to January, 2026 for studies comparing GLS to LVEF for detecting CTRCD in cancer patients. Data on study characteristics, patient demographics, cancer and therapy types, diagnostic accuracy, and prognostic value were extracted. The risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e47 studies were included. The evidence consistently demonstrates the superior sensitivity of GLS over LVEF for the early detection of subclinical CTRCD. GLS changes often preceded a significant LVEF decline by weeks to months. A preserved GLS had a high negative predictive value (often\u0026thinsp;\u0026gt;\u0026thinsp;90\u0026ndash;95%) for ruling out future LVEF-defined cardiotoxicity. Furthermore, abnormal GLS was associated with a higher risk of subsequent clinical heart failure events. One randomized controlled trial (the SUCCOUR trial) showed that a GLS-guided management strategy reduced the incidence of CTRCD compared to an LVEF-guided strategy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGLS is a more sensitive and prognostically valuable tool than LVEF for the early identification of subclinical cardiotoxicity. Its integration into routine surveillance protocols for patients receiving cardiotoxic cancer therapy is strongly supported by the evidence. A GLS-guided approach for initiating cardioprotective therapy should be considered to improve cardiac outcomes in oncology patients.\u003c/p\u003e","manuscriptTitle":"Global Longitudinal Strain Compared to Left Ventricular Ejection Fraction for Early Identification of Subclinical Cardiotoxicity in Cancer Patients: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 06:21:18","doi":"10.21203/rs.3.rs-9242876/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3ed58b4-e9b2-49ba-9f8f-d5c86486d086","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T15:12:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 06:21:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9242876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9242876","identity":"rs-9242876","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}