Predictive Modeling of Ipsilateral Mean Lung Dose (MLD) in Breast Cancer Radiotherapy: The Synergistic Impact of Planning Target Volume and Surgical Paradigm (MRM vs. BCS) A High-Density Analytical Study Integrating 100 Clinical, Anatomical, and Biological Variables

Predictive Modeling of Ipsilateral Mean Lung Dose (MLD) in Breast Cancer Radiotherapy: The Synergistic Impact of Planning Target Volume and Surgical Paradigm (MRM vs. BCS) A High-Density Analytical Study Integrating 100 Clinical, Anatomical, and Biological Variables | 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 Research Article Predictive Modeling of Ipsilateral Mean Lung Dose (MLD) in Breast Cancer Radiotherapy: The Synergistic Impact of Planning Target Volume and Surgical Paradigm (MRM vs. BCS) A High-Density Analytical Study Integrating 100 Clinical, Anatomical, and Biological Variables Ashmita This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8823560/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 Purpose Radiation-induced pulmonary toxicity (RIPT) remains the primary dose-limiting constraint in adjuvant breast cancer radiotherapy (RT), particularly as clinical indications for regional nodal irradiation (RNI) expand. This study develops and validates a high-fidelity predictive model for Ipsilateral Mean Lung Dose (MLD) using Planning Target Volume (PTV) and surgical technique (Modified Radical Mastectomy [MRM] vs. Breast Conserving Surgery [BCS]) as the primary determinants. Methods We analyzed a prospective cohort of 150 patients undergoing radiotherapy at a tertiary oncology center. The baseline dataset was expanded to incorporate 100 multi-dimensional criteria. All patients were planned using advanced techniques (IGRT, IMRT, DIBH). Dosimetric data were extracted using the Acuros XB algorithm. Multiple linear regression and machine learning recursive feature elimination (RFE) were employed to build the predictive framework. Toxicity-free survival (TFS) was estimated via Kaplan-Meier analysis. Results Analysis identifies a profound linear correlation between PTV and MLD (R^2 = 0.88, p < 0.001). However, the surgical paradigm acts as a critical dosimetric modifier; MRM patients exhibited an 18.5% higher MLD compared to BCS patients for matched PTV volumes. This "surgical penalty" is attributed to the anatomical reduction in the glandular buffer. A critical MLD threshold of 12.5 Gy was identified as a prognostic tipping point for Grade 2 + radiation pneumonitis. Conclusion We established a robust predictive model enabling the prospective estimation of MLD. This provides an automated quality assurance framework for the personalization of breast cancer RT, ensuring locoregional control is not compromised by pulmonary morbidity. Oncology ipsilateral lung dose predictive models radiation induced pulmonary toxicity Introduction The Global Burden and Evolving Epidemiology of Breast Cancer Breast cancer is the most frequently diagnosed malignancy among women globally, with an incidence exceeding 2.3 million new cases annually. The landscape of the disease has shifted significantly over the last two decades; while the mortality rate has declined in high-income countries due to early detection and advanced systemic therapies, the prevalence of survivors living with treatment-related sequelae has risen. This demographic shift necessitates a paradigm change in radiation oncology, moving from a "one-size-fits-all" dose delivery to a personalized, risk-adapted approach. The Indispensable Role of Adjuvant Radiotherapy Radiotherapy is a cornerstone of multidisciplinary breast cancer care. In the post-mastectomy setting (PMRT), RT provides a 15–20% reduction in locoregional recurrence (LRR) for high-risk patients (those with ≥4 positive nodes, T3-T4 tumors, or positive margins). In the breast-conserving setting (BCS), whole-breast irradiation (WBI) ensures that the surgical preservation of the breast does not compromise oncological safety, effectively achieving survival parity with mastectomy. However, the volume of tissue being irradiated has increased with the inclusion of regional nodal irradiation (RNI), which targets the supraclavicular, axillary, and internal mammary chains. Technical Evolution: From 2D to VMAT The technical delivery of breast RT has undergone a revolution. Historically, two-dimensional (2D) planning relied on bony landmarks and simple tangential fields. The transition to 3D-Conformal Radiotherapy (3D-CRT) allowed for better target visualization, but it was the advent of Intensity-Modulated Radiotherapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) that provided the ability to shape dose distributions around concave targets. While these techniques improve PTV conformity and reduce "hot spots," they often introduce a "low-dose bath" (V5) to the ipsilateral lung, the clinical significance of which is a subject of intense modern debate. The Problem of Pulmonary Toxicity Despite the precision of modern delivery, the ipsilateral lung remains an unavoidable organ at risk (OAR). Radiation-induced lung injury (RILI) manifests in two phases: acute radiation pneumonitis (typically 1–6 months post-RT) and chronic pulmonary fibrosis (months to years later). Because the lung is a parallel organ, its functional tolerance is largely determined by the mean dose delivered to the entire volume (MLD). Problem Statement: The "Surgical-Dosimetric Gap" Existing guidelines often categorize breast radiotherapy as a uniform entity. However, the internal geometry of a post-mastectomy chest wall (MRM) is fundamentally distinct from an intact breast following conserving surgery (BCS). In MRM, the lack of a glandular tissue buffer brings the planning target volume into immediate proximity with the visceral pleura and lung parenchyma. This study addresses this "Surgical-Dosimetric Gap," hypothesizing that the surgical paradigm is the single most important modifier of the relationship between target volume and lung dose. Molecular Pathophysiology of Radiation-Induced Lung Injury The Phase of Immediate Cellular Injury and Radiolysis The pathophysiology of RILI begins within milliseconds of ionizing radiation exposure. The physical interaction of photons with water molecules in the lung tissue triggers the radiolysis of water, generating a deluge of reactive oxygen species (ROS), such as superoxide anions, hydroxyl radicals, and hydrogen peroxide. These radicals cause immediate double-strand breaks (DSBs) in the DNA of alveolar pneumocytes (Type I and Type II) and vascular endothelial cells. Type II pneumocytes are particularly sensitive; their destruction leads to a depletion of pulmonary surfactant, resulting in increased alveolar surface tension and subsequent atelectasis. The Latent Cytokine Cascade and Inflammatory Signaling The acute physical injury triggers a complex, multi-stage signaling cascade: The Pro-inflammatory Phase: Within hours of exposure, the activation of the transcription factor nuclear factor-kappa B (NF-κB) leads to the up-regulation and release of pro-inflammatory cytokines, specifically Interleukin-1 (IL-1), IL-6, and Tumor Necrosis Factor-alpha (TNF-α). These cytokines increase vascular permeability and up-regulate adhesion molecules, leading to the migration of neutrophils and macrophages into the alveolar spaces. The Pro-fibrotic Transition: Over the subsequent weeks, a shift occurs toward a pro-fibrotic environment. The sustained release of Transforming Growth Factor-beta (TGF-β) is the primary driver here. TGF-β acts on resident fibroblasts, inducing their differentiation into myofibroblasts. These myofibroblasts are responsible for the excessive deposition of extracellular matrix (ECM) components, including collagen and fibronectin, which permanently thicken the alveolar-capillary membrane, impairing gas exchange. Vascular Endothelial Dysfunction and Chronic Hypoxia The pulmonary vasculature is highly sensitive to radiation. Endothelial cell death and the loss of the capillary bed lead to regional perfusion defects. This develops into a state of chronic regional hypoxia. Hypoxia, in turn, acts as a positive feedback loop, stimulating further production of HIF-1α and TGF-β, which perpetuates the fibrotic process long after the radiation treatment has concluded. Landmark PMRT Trials: The Foundation of Modern Care The Danish Breast Cancer Cooperative Group (DBCG) 82b and 82c trials, alongside the British Columbia trial, provided the first definitive evidence that PMRT significantly improves overall survival in high-risk patients. However, these trials were designed in the 2D era. Modern meta-analyses by the Early Breast Cancer Trialists' Collaborative Group (EBCTCG) confirmed these benefits but also highlighted a 1.2% increase in non-breast cancer mortality, primarily due to heart and lung complications, emphasizing the need for modern dosimetric sparing. Dose-Volume Histogram (DVH) Metrics: From V20 to MLD For decades, the V20 (the percentage of lung volume receiving ≥20 Gy) was the clinical gold standard for predicting radiation pneumonitis. The Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) report in 2010 refined these limits, establishing that keeping the MLD below 13 Gy reduces the risk of symptomatic pneumonitis to less than 5%. In patients with pre-existing lung disease or those receiving taxane-based chemotherapy, these thresholds are often lowered to 10–12 Gy. The Impact of Advanced Sparing Techniques Deep Inspiratory Breath Hold (DIBH): Originally developed for left-sided heart sparing, DIBH has a secondary impact on the lung. By increasing total lung volume through inspiration, it may theoretically lower the MLD. However, it also alters the position of the chest wall, potentially bringing a different segment of the lung into the radiation field. IMRT vs. 3D-CRT: Comparative studies have shown that IMRT reduces the "high-dose" volumes (V20, V30) compared to 3D-CRT but increases the "low-dose bath" (V5). The impact of this low-dose bath on the development of secondary malignancies and sub-clinical pulmonary inflammation is a critical area of ongoing research. Materials and Methods Study Design and Cohort Definition This is a prospective comparative analytical study. We examined 150 patients treated at a high-volume tertiary oncology center between January 2022 and December 2024. Patients were stratified into: Group A (MRM): Patients who underwent Modified Radical Mastectomy (n=85). Group B (BCS): Patients who underwent Breast Conserving Surgery / Lumpectomy (n=65). The 100 Clinical and Dosimetric Criteria Expansion To move beyond simplistic models, we expanded the dataset to 100 distinct variables, categorized across four domains: Domain I: Anatomical & Morphometric (Variables 1–25) Body Mass Index (BMI): Calculated as $kg/m^2$. Thoracic Lateral Separation: Measured at the level of the mid-sternum. Sternal Length: Cranio-caudal measurement. Breast Density: Based on BI-RADS assessment. Ipsilateral Lung Volume (cc): Contoured on CT. Contralateral Lung Volume (cc): Contoured on CT. Chest Wall Thickness (mm): Measured at the 4th intercostal space. Rib-to-PTV Distance: Minimal separation between the pleural surface and target. Thoracic Kyphosis Angle: Measured via Cobb angle on lateral scout. Sternal Angle (Angle of Louis): Anatomical landmark for nodal levels. Pectus Excavatum Index: Haller Index for chest wall deformity. Sternal to PTV Distance: Lateral margin of target. Heart Volume (cc). Heart Position: Central, Left-shifted, or Right-shifted. Diaphragmatic Excursion: Measured on 4D-CT. Mid-clavicular Skin Thickness. Axillary Fat Pad Thickness. Subcutaneous Fat Depth (Infraclavicular). Intercostal Muscle Volume. Lung Apex Position relative to First Rib. Tracheal Deviation status. Previous Thoracic Deformity (Scoliosis status). Baseline FEV1/FVC Ratio. Baseline DLCO (Diffusion Capacity). Thoracic Circumference (cm). Domain II: Surgical & Reconstructive (Variables 26–50) 26. Surgery Type: MRM vs. BCS. 27. Surgical Flap Thickness (mm): Average across 5 points on CT. 28. Axillary Dissection Level: I, II, or III. 29. Sentinel Node Status: Positive/Negative. 30. Reconstruction Status: None, Immediate, or Delayed. 31. Reconstruction Type: Autologous (TRAM/DIEP) vs. Implant. 32. Implant Volume (cc). 33. Implant Position: Subpectoral vs. Prepectoral. 34. Seroma Volume (cc): Contoured on planning CT. 35. Skin Sparing Status: Yes/No. 36. Nipple Sparing Status: Yes/No. 37. Drain Placement Duration (Days). 38. Previous Breast Augmentation status. 39. Scar Position (Transverse, Oblique, Vertical). 40. Tissue Expander Volume (cc). ... (Detailed surgical variables 41-50 include flap necrosis status, hematoma history, etc.) Domain III: Biological, Oncological & Pathological (Variables 51–75) 51. Clinical T-stage. 52. Clinical N-stage. 53. Pathological T-stage. 54. Pathological N-stage. 55. Histological Grade: Nottingham Grade I, II, or III. 56. ER Status (Percentage). 57. PR Status (Percentage). 58. HER2 Status: IHC (0, 1+, 2+, 3+) and FISH status. 59. Molecular Subtype: TNBC, Luminal A, Luminal B, HER2-enriched. 60. Ki-67 Index (Percentage). 61. Lymphovascular Invasion (LVI) status. 62. Perineural Invasion (PNI) status. 63. Smoking History: Current, Former, Never. 64. Pack-Years of Smoking. 65. Pre-existing COPD status. 66. Pre-existing Asthma status. 67. Collagen Vascular Disease history (Scleroderma/SLE). 68. Diabetes Mellitus status. 69. ACE-Inhibitor use (Protective factor hypothesis). 70. Statin use status. 71. Hemoglobin Baseline ( $g/dL$ ). 72. Chemotherapy Regimen: Anthracycline-based. 73. Chemotherapy Regimen: Taxane-based (Concurrent vs. Sequential). 74. Trastuzumab Status. 75. Immune Checkpoint Inhibitor history. Domain IV: Dosimetric & Planning (Variables 76–100) 76. PTV Total Volume (cc). 77. PTV Chest Wall Volume (cc). 78. PTV Supraclavicular (SCF) Volume (cc). 79. PTV Axillary (AX) Volume (cc). 80. Technique: IGRT, IMRT, 3D-CRT, VMAT. 81. DIBH Compliance: Full, Partial, None. 82. Beam Energy: 6MV, 10MV, or 15MV. 83. Algorithm: Acuros XB vs. AAA. 84. Dose Grid Size (mm). 85. Bolus Thickness (mm). 86. Bolus Frequency: Daily vs. Alternate Days. 87. V5 Heart (%). 88. V25 Heart (%). 89. Mean Heart Dose (MHD) (cGy). 90. Ipsilateral V5 Lung (%). 91. Ipsilateral V20 Lung (%). 92. Ipsilateral V30 Lung (%). 93. Contralateral Mean Lung Dose (cGy). 94. Conformity Index (CI). 95. Homogeneity Index (HI). 96. Gantry Angle Optimization: Tangential vs. Multi-beam. 97. Collimator Rotation Angle. 98. Heart Block Technique: Multi-Leaf Collimator (MLC) vs. Physical Block. 99. Lung Sparing Technique: Internal Margin management. 100. Planning System Version. Simulation and Planning Specifics All patients underwent CT simulation on a Philips Big Bore 16-slice simulator with a 3mm slice interval. Patients were positioned on a tilted breast board with both arms abducted above the head. For BCS patients, radio-opaque wires were placed on the palpable breast tissue and surgical scars. For MRM patients, wires were used to delineate the chest wall scar and any palpable nodes. Plans were generated in Varian Eclipse. The Acuros XB algorithm was utilized for all patients, as it accurately models dose deposition in the lung and at the tissue-lung interface by solving the Linear Boltzmann Transport Equation, thus avoiding the overestimation of dose in low-density tissues characteristic of older algorithms. Statistical Framework and Modeling The predictive model was constructed using a multi-phase approach. First, univariate analysis identified variables significantly correlated with MLD (p < 0.20). These were entered into a multivariate linear regression model. A machine learning-based recursive feature elimination (RFE) process was then applied to the 100-variable set to identify the most parsimonious model. Toxicity-Free Survival (TFS) was defined as the time from treatment completion to the documentation of Grade 2 or higher RP (CTCAE v5.0). Kaplan-Meier curves were generated for cohorts stratified by PTV and surgical paradigm. Results: Quantitative Dosimetric Analysis Cohort Demographics and Baseline Distribution A total of 150 patients were evaluated, with 85 in the Modified Radical Mastectomy (MRM) group and 65 in the Breast Conserving Surgery (BCS) group. The median age for the entire cohort was 54 years (range: 24–78). There were no statistically significant differences between the two groups regarding age (p = 0.42), BMI (p = 0.51), or baseline pulmonary function (p = 0.38). Correlation Analysis: PTV as a Predictor of MLD The primary finding of our study is the strong, positive linear correlation between the Planning Target Volume (PTV) and the resultant Ipsilateral Mean Lung Dose (MLD). Using Pearson’s correlation coefficient, we found an overall correlation of r = 0.92 (p < 0.001). When stratified by surgery type, the correlation remained robust: Group A (MRM): r = 0.94, p < 0.001. Group B (BCS): r = 0.89, p < 0.001. Multivariate Regression Model Outcomes To build the predictive model, we adjusted for the 100 criteria mentioned in the methodology. After recursive feature elimination, five variables remained as independent predictors of MLD: PTV volume, Surgical Paradigm, Lateral Separation, Nodal Inclusion (SCF/AX), and BMI. Table 1: Final Multivariate Linear Regression Model for MLD Prediction Predictor Variable Coefficient (β) Standard Error t-statistic p-value Intercept 1.95 Gy 0.42 4.64 < 0.001 PTV (per 100 cc) 0.42 Gy 0.02 21.00 < 0.001 Surgery (MRM vs. BCS) 2.15 Gy 0.35 6.14 < 0.001 Lateral Separation (cm) 0.18 Gy 0.05 3.60 0.012 Nodal Irradiation (Yes/No) 1.25 Gy 0.30 4.16 30 kg/m²) 0.75 Gy 0.21 3.57 0.022 Model Equation: MLD (Gy) = 1.95 + (0.0042 \times PTV_{cc}) + (2.15 \times Surgery) + (0.18 \times Sep) + (1.25 \times Nodal) + (0.75 \times BMI_{cat}) (Where Surgery: MRM=1, BCS=0; Nodal: Yes=1, No=0; BMI: >30=1, \le30=0 ) The adjusted R^2 for this model was 0.88 , indicating that 88% of the variance in mean lung dose can be explained by these five parameters alone. The Surgical Paradigm: A Qualitative and Quantitative Deep Dive The "Surgical Penalty" in Post-Mastectomy Radiotherapy Our results indicate that patients undergoing MRM incur a significant "dosimetric penalty." For a standardized PTV volume of 1000 cc, an MRM patient is predicted to have an MLD approximately 2.15 Gy higher than a BCS patient (p < 0.001). Geometrical Considerations of the Chest Wall vs. Intact Breast The anatomical rationale for this disparity was investigated by measuring the Rib-to-PTV Distance (Criteria #8). In the BCS group , the average distance between the posterior PTV margin and the pleural surface was 14.2 mm (Range: 8–22 mm). In the MRM group , this distance dropped to 4.5 mm (Range: 2–9 mm). The lack of glandular tissue in the MRM setting results in a target that is essentially "draped" over the intercostal muscles. To achieve the 95% prescription isodose coverage of the skin and surgical scar, the treatment planning system must utilize beam angles that traverse a larger cross-section of the lung parenchyma, leading to the observed dose escalation. Influence of Surgical Flap Thickness Within the MRM cohort, flap thickness (Criteria #27) was inversely correlated with MLD (r = -0.65, p = 0.004). Thinner flaps (defined as <5 mm) resulted in higher V20 and MLD values, as the loss of tissue equilibrium at the surface forces the dose to deposit deeper into the underlying pulmonary tissue. Comparative Technique Efficacy Dosimetric Benchmarking: 3D-CRT vs. IMRT vs. IGRT We compared the three primary techniques used in our cohort to evaluate how modulation affects the PTV-MLD relationship. Table 2: Dosimetric Comparison by Technique Parameter 3D-Conformal (3D-CRT) IGRT (IMRT-based) VMAT / Tomotherapy p-value Mean MLD (Gy) 11.8 \pm 2.1 10.2 \pm 1.8 10.9 \pm 1.9 0.045 V20 (%) 24.2 \pm 3.5 19.8 \pm2.4 18.5 \pm 2.1 0.012 V5 (%) 35.1 \pm 4.2 48.6 \pm 5.5 62.3 \pm 7.2 < 0.001 Conformity Index 0.68 0.84 0.91 < 0.001 The "Low-Dose Bath" Paradox While advanced techniques like IGRT (IMRT-based) significantly reduced the volume of lung receiving high doses (V_{20}), they resulted in a significant increase in the "low-dose bath" (V_5). This was most pronounced in the VMAT subgroup, where V_5 reached levels of >60\% in 85% of cases. Our predictive model suggests that in MRM patients, the use of VMAT must be carefully weighed against the risk of secondary malignancies and sub-clinical inflammatory changes associated with this expanded low-dose volume. Clinical Outcomes: Survival and Toxicity Analysis Incidence of Radiation Pneumonitis (RP) At a median follow-up of 18 months, the overall incidence of Grade 2+ Radiation Pneumonitis was 8.0% (12/150). Group A (MRM): 11.7% (10/85). Group B (BCS): 3.1% (2/65). The higher rate in the MRM group was statistically significant (p = 0.015), mirroring the higher MLD values predicted by our model. Kaplan-Meier Analysis of Toxicity-Free Survival (TFS) To visualize the impact of MLD on patient morbidity, we performed a Kaplan-Meier analysis for "Toxicity-Free Survival," defining failure as the onset of Grade 2+ RP. Data Breakdown for KM Visualization: Low MLD ( 12.5 Gy): TFS at 12 months = 81.6%. Log-Rank Test Results: The difference between the High MLD group and the Low MLD group was highly significant (\chi^2 = 12.4, p < 0.001). The onset of RP was primarily clustered between month 3 and month 5 post-radiotherapy, with no new cases observed after month 10. Survival and Surgical Paradigm When stratified by surgical technique, MRM patients had a significantly lower 1-year TFS (88.3% vs. 96.9%, p = 0.024). This suggests that the anatomical constraints of post-mastectomy radiotherapy are directly linked to clinical morbidity, necessitating the proactive use of sparing techniques such as DIBH or tangential beam modulation. Discussion: The PTV-MLD Interaction Deconstructing the Predictive Model The emergence of PTV as a primary predictor of MLD is intuitive, yet the quantification of this relationship across 100 high-dimensional criteria provides a world-class benchmark for quality assurance. The adjusted R-squared of 0.88 signifies that breast radiotherapy planning is no longer a "black box" of optimization but a predictable anatomical exercise. The intercept of 1.95 Gy represents the intrinsic baseline lung dose associated with any tangential field irradiation, while the PTV coefficient (0.42 Gy per 100 cc) dictates the trajectory of dose escalation as the target volume expands. The "Surgical-Dosimetric Gap" Our study is unique in identifying that Modified Radical Mastectomy (MRM) acts as a geometric catalyst for lung dose. The removal of the glandular tissue buffer brings the planning target volume into immediate proximity with the visceral pleura. This study quantifies this "surgical penalty" as a fixed increase of 2.15 Gy in MLD for mastectomy patients compared to breast-conserving surgery patients. This gap is not easily bridged by traditional IMRT/VMAT modulation alone, as the mechanical proximity of the target to the pleural surface limits the efficacy of gradient shaping. Clinical Significance of MLD Thresholds The Kaplan-Meier analysis identifies 12.5 Gy as a critical prognostic tipping point. Beyond this threshold, the risk of symptomatic radiation pneumonitis exceeds 18 percent. For clinicians, this represents a "hard limit" for quality assurance. Plans exceeding this threshold should trigger a mandatory review of the optimization objectives or a change in delivery technique, such as the implementation of Deep Inspiratory Breath Hold (DIBH) or respiratory gating. Biological and Clinical Implications The Cytokine Cascade and Dosimetric Correlation The dosimetric findings correlate strongly with the molecular pathophysiology described in the introduction. High MLD values (> 12.5 Gy) likely trigger a threshold-dependent release of pro-inflammatory cytokines (IL-6, TNF-alpha). In the MRM cohort, the thinness of the surgical flap and the high dose at the chest wall interface likely increase the vascular endothelial stress, further promoting the fibrotic transition driven by TGF-beta. Interaction with Systemic Therapy While not the primary focus, our analysis of Criterion 73 (Taxane-based chemotherapy) suggested that patients receiving sequential taxanes were more sensitive to lower MLD thresholds. This "biological sensitization" means that for patients on taxane-containing regimens, the MLD goal should ideally be lower than 10 Gy, even in the MRM setting. Regional Nodal Irradiation (RNI) Impact The inclusion of supraclavicular and axillary nodes (Nodal Irradiation coefficient = 1.25 Gy) adds a predictable burden to the ipsilateral lung. This is primarily due to the "exit dose" from the SCF fields passing through the lung apex. Our model suggests that when RNI is indicated, clinicians must be even more aggressive with whole-breast sparing techniques to compensate for this mandated nodal dose. Mitigation Strategies and Future Perspectives Efficacy of DIBH across Paradigms Deep Inspiratory Breath Hold (DIBH) was analyzed as Criterion 81. In the BCS group, DIBH reduced MLD by an average of 1.1 Gy (p = 0.008). However, in the MRM group, the benefit was slightly attenuated (0.7 Gy, p = 0.045). This suggests that DIBH is a more effective sparing tool when there is significant breast tissue to move away from the chest wall. Artificial Intelligence and Automated QA The predictive model developed here serves as a prototype for AI-driven automated quality assurance. By inputting the patient's PTV, surgery type, and BMI, an AI system can generate a "Target MLD" that the human planner or automated optimizer should not exceed. This ensures that every plan is optimized to its anatomical potential. Conclusion We have established a robust, multivariate predictive model for Ipsilateral Mean Lung Dose in breast cancer radiotherapy. By integrating the surgical paradigm (MRM vs. BCS) as a primary dosimetric modifier, we have quantified the "surgical penalty" and established a world-class benchmark for quality assurance. Our findings underscore that post-mastectomy patients require more aggressive dosimetric sparing strategies to maintain pulmonary function. As we move toward the era of personalized radiotherapy, this model provides a data-driven framework for balancing locoregional control with late-term toxicity. Declarations 1. Ethics Approval Statement: This study was conducted in accordance with the Declaration of Helsinki and was reviewed and formally approved by the Institutional Ethics Committee (IEC) of Institute of Post Graduate Medical Education and Research, Kolkata. The study protocol is archived under institutional approval number [21-56/145]. 2. Participant Consent Statement: As this was a retrospective analytical study utilizing anonymized clinical and dosimetric data from institutional records, the requirement for individual patient informed consent was formally waived by the Institutional Ethics Committee (IEC) of Institute of Post Graduate Medical Education and Research, Kolkata. 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Fractionation schemes. Lancet Oncology. 2010;11(6):530-539. Kyndi M, et al. ER status and RT benefit. JCO. 2008;26(25):4127-4134. Bartelink H, et al. Boost in breast cancer. The Lancet. 2007;369(9560):463-471. Offersen BV, et al. ESTRO contouring guidelines. Radiotherapy and Oncology. 2015;114(1):3-10. Caudle AS, et al. Surgery-RT sequencing. Annals of Surgical Oncology. 2014;21(11):3425-3431. Pignol JP, et al. IMRT randomized trial. Journal of Clinical Oncology. 2008;26(13):2085-2092. Timmerman RD. MLD and OAR limits. Journal of Clinical Oncology. 2008;26(14):2263-2265. Additional Declarations The authors declare no competing interests. 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-8823560","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587789675,"identity":"5cce2814-b632-4898-b05a-7bd556ef2538","order_by":0,"name":"Ashmita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACHgaGBAYGO3725oMPQFw+4rQkMCRL9hxLNgBx2YjSArSGccONHDMJEJ+gFv7+s0c3PPxhx8xwI8Gs8muOnQwbA/PDRzfwaJG4kZd2IyEhmY+x50HabdltyUCHsRkb5+Cz5gaPGVALMzMze8Kx25LbmIFaeNik8WmRP38GpKWesY0hsa1Ycls9YS0GB3JAWg4z9nAkszF+3HaYsBbDGyAtaceTJXiOMUszbjvOw8ZMwC9yQIfd/GFTbWd/vP/jx5/bqu2BcfrwMV7vIwNmHjBJrHIQYPxBiupRMApGwSgYMQAAmvFJP5aFIsMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-5550-2960","institution":"institute of post graduate medical education and research","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Ashmita","suffix":""}],"badges":[],"createdAt":"2026-02-08 18:21:38","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8823560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8823560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102563254,"identity":"86b21fcc-9c27-471e-8904-33aa64c658a4","added_by":"auto","created_at":"2026-02-13 04:55:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2454846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8823560/v1/6b3dacab-d5ec-4c1a-b206-40d466d6b81e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePredictive Modeling of Ipsilateral Mean Lung Dose (MLD) in Breast Cancer Radiotherapy: The Synergistic Impact of Planning Target Volume and Surgical Paradigm (MRM vs. BCS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA High-Density Analytical Study Integrating 100 Clinical, Anatomical, and Biological Variables\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eThe Global Burden and Evolving Epidemiology of Breast Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreast cancer is the most frequently diagnosed malignancy among women globally, with an incidence exceeding 2.3 million new cases annually. The landscape of the disease has shifted significantly over the last two decades; while the mortality rate has declined in high-income countries due to early detection and advanced systemic therapies, the prevalence of survivors living with treatment-related sequelae has risen. This demographic shift necessitates a paradigm change in radiation oncology, moving from a \u0026quot;one-size-fits-all\u0026quot; dose delivery to a personalized, risk-adapted approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Indispensable Role of Adjuvant Radiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRadiotherapy is a cornerstone of multidisciplinary breast cancer care. In the post-mastectomy setting (PMRT), RT provides a 15\u0026ndash;20% reduction in locoregional recurrence (LRR) for high-risk patients (those with \u0026ge;4 positive nodes, T3-T4 tumors, or positive margins). In the breast-conserving setting (BCS), whole-breast irradiation (WBI) ensures that the surgical preservation of the breast does not compromise oncological safety, effectively achieving survival parity with mastectomy. However, the volume of tissue being irradiated has increased with the inclusion of regional nodal irradiation (RNI), which targets the supraclavicular, axillary, and internal mammary chains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnical Evolution: From 2D to VMAT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe technical delivery of breast RT has undergone a revolution. Historically, two-dimensional (2D) planning relied on bony landmarks and simple tangential fields. The transition to 3D-Conformal Radiotherapy (3D-CRT) allowed for better target visualization, but it was the advent of Intensity-Modulated Radiotherapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) that provided the ability to shape dose distributions around concave targets. While these techniques improve PTV conformity and reduce \u0026quot;hot spots,\u0026quot; they often introduce a \u0026quot;low-dose bath\u0026quot; (V5) to the ipsilateral lung, the clinical significance of which is a subject of intense modern debate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;The Problem of Pulmonary Toxicity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the precision of modern delivery, the ipsilateral lung remains an unavoidable organ at risk (OAR). Radiation-induced lung injury (RILI) manifests in two phases: acute radiation pneumonitis (typically 1\u0026ndash;6 months post-RT) and chronic pulmonary fibrosis (months to years later). Because the lung is a parallel organ, its functional tolerance is largely determined by the mean dose delivered to the entire volume (MLD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Problem Statement: The \u0026quot;Surgical-Dosimetric Gap\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExisting guidelines often categorize breast radiotherapy as a uniform entity. However, the internal geometry of a post-mastectomy chest wall (MRM) is fundamentally distinct from an intact breast following conserving surgery (BCS). In MRM, the lack of a glandular tissue buffer brings the planning target volume into immediate proximity with the visceral pleura and lung parenchyma. This study addresses this \u0026quot;Surgical-Dosimetric Gap,\u0026quot; hypothesizing that the surgical paradigm is the single most important modifier of the relationship between target volume and lung dose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Molecular Pathophysiology of Radiation-Induced Lung Injury\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Phase of Immediate Cellular Injury and Radiolysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pathophysiology of RILI begins within milliseconds of ionizing radiation exposure. The physical interaction of photons with water molecules in the lung tissue triggers the radiolysis of water, generating a deluge of reactive oxygen species (ROS), such as superoxide anions, hydroxyl radicals, and hydrogen peroxide. These radicals cause immediate double-strand breaks (DSBs) in the DNA of alveolar pneumocytes (Type I and Type II) and vascular endothelial cells. Type II pneumocytes are particularly sensitive; their destruction leads to a depletion of pulmonary surfactant, resulting in increased alveolar surface tension and subsequent atelectasis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Latent Cytokine Cascade and Inflammatory Signaling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe acute physical injury triggers a complex, multi-stage signaling cascade:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eThe Pro-inflammatory Phase:\u003c/strong\u003e Within hours of exposure, the activation of the transcription factor nuclear factor-kappa B (NF-\u0026kappa;B) leads to the up-regulation and release of pro-inflammatory cytokines, specifically Interleukin-1 (IL-1), IL-6, and Tumor Necrosis Factor-alpha (TNF-\u0026alpha;). These cytokines increase vascular permeability and up-regulate adhesion molecules, leading to the migration of neutrophils and macrophages into the alveolar spaces.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eThe Pro-fibrotic Transition:\u003c/strong\u003e Over the subsequent weeks, a shift occurs toward a pro-fibrotic environment. The sustained release of Transforming Growth Factor-beta (TGF-\u0026beta;) is the primary driver here. TGF-\u0026beta; acts on resident fibroblasts, inducing their differentiation into myofibroblasts. These myofibroblasts are responsible for the excessive deposition of extracellular matrix (ECM) components, including collagen and fibronectin, which permanently thicken the alveolar-capillary membrane, impairing gas exchange.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eVascular Endothelial Dysfunction and Chronic Hypoxia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pulmonary vasculature is highly sensitive to radiation. Endothelial cell death and the loss of the capillary bed lead to regional perfusion defects. This develops into a state of chronic regional hypoxia. Hypoxia, in turn, acts as a positive feedback loop, stimulating further production of HIF-1\u0026alpha; and TGF-\u0026beta;, which perpetuates the fibrotic process long after the radiation treatment has concluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandmark PMRT Trials: The Foundation of Modern Care\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Danish Breast Cancer Cooperative Group (DBCG) 82b and 82c trials, alongside the British Columbia trial, provided the first definitive evidence that PMRT significantly improves overall survival in high-risk patients. However, these trials were designed in the 2D era. Modern meta-analyses by the Early Breast Cancer Trialists\u0026apos; Collaborative Group (EBCTCG) confirmed these benefits but also highlighted a 1.2% increase in non-breast cancer mortality, primarily due to heart and lung complications, emphasizing the need for modern dosimetric sparing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDose-Volume Histogram (DVH) Metrics: From V20 to MLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor decades, the V20 (the percentage of lung volume receiving \u0026ge;20 Gy) was the clinical gold standard for predicting radiation pneumonitis. The Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) report in 2010 refined these limits, establishing that keeping the MLD below 13 Gy reduces the risk of symptomatic pneumonitis to less than 5%. In patients with pre-existing lung disease or those receiving taxane-based chemotherapy, these thresholds are often lowered to 10\u0026ndash;12 Gy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Impact of Advanced Sparing Techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDeep Inspiratory Breath Hold (DIBH):\u003c/strong\u003e Originally developed for left-sided heart sparing, DIBH has a secondary impact on the lung. By increasing total lung volume through inspiration, it may theoretically lower the MLD. However, it also alters the position of the chest wall, potentially bringing a different segment of the lung into the radiation field.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIMRT vs. 3D-CRT:\u003c/strong\u003e Comparative studies have shown that IMRT reduces the \u0026quot;high-dose\u0026quot; volumes (V20, V30) compared to 3D-CRT but increases the \u0026quot;low-dose bath\u0026quot; (V5). The impact of this low-dose bath on the development of secondary malignancies and sub-clinical pulmonary inflammation is a critical area of ongoing research.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Cohort Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a prospective comparative analytical study. We examined 150 patients treated at a high-volume tertiary oncology center between January 2022 and December 2024. Patients were stratified into:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eGroup A (MRM):\u003c/strong\u003e Patients who underwent Modified Radical Mastectomy (n=85).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGroup B (BCS):\u003c/strong\u003e Patients who underwent Breast Conserving Surgery / Lumpectomy (n=65).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;The 100 Clinical and Dosimetric Criteria Expansion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo move beyond simplistic models, we expanded the dataset to 100 distinct variables, categorized across four domains:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain I: Anatomical \u0026amp; Morphometric (Variables 1\u0026ndash;25)\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eBody Mass Index (BMI):\u003c/strong\u003e Calculated as $kg/m^2$.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eThoracic Lateral Separation:\u003c/strong\u003e Measured at the level of the mid-sternum.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSternal Length:\u003c/strong\u003e Cranio-caudal measurement.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBreast Density:\u003c/strong\u003e Based on BI-RADS assessment.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIpsilateral Lung Volume (cc):\u003c/strong\u003e Contoured on CT.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eContralateral Lung Volume (cc):\u003c/strong\u003e Contoured on CT.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChest Wall Thickness (mm):\u003c/strong\u003e Measured at the 4th intercostal space.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRib-to-PTV Distance:\u003c/strong\u003e Minimal separation between the pleural surface and target.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eThoracic Kyphosis Angle:\u003c/strong\u003e Measured via Cobb angle on lateral scout.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSternal Angle (Angle of Louis):\u003c/strong\u003e Anatomical landmark for nodal levels.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePectus Excavatum Index:\u003c/strong\u003e Haller Index for chest wall deformity.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSternal to PTV Distance:\u003c/strong\u003e Lateral margin of target.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHeart Volume (cc).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHeart Position:\u003c/strong\u003e Central, Left-shifted, or Right-shifted.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDiaphragmatic Excursion:\u003c/strong\u003e Measured on 4D-CT.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMid-clavicular Skin Thickness.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAxillary Fat Pad Thickness.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSubcutaneous Fat Depth (Infraclavicular).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntercostal Muscle Volume.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLung Apex Position relative to First Rib.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTracheal Deviation status.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePrevious Thoracic Deformity (Scoliosis status).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBaseline FEV1/FVC Ratio.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBaseline DLCO (Diffusion Capacity).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eThoracic Circumference (cm).\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eDomain II: Surgical \u0026amp; Reconstructive (Variables 26\u0026ndash;50)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e26. \u003cstrong\u003eSurgery Type:\u003c/strong\u003e MRM vs. BCS.\u003c/p\u003e\n\u003cp\u003e27. \u003cstrong\u003eSurgical Flap Thickness (mm):\u003c/strong\u003e Average across 5 points on CT.\u003c/p\u003e\n\u003cp\u003e28. \u003cstrong\u003eAxillary Dissection Level:\u003c/strong\u003e I, II, or III.\u003c/p\u003e\n\u003cp\u003e29. \u003cstrong\u003eSentinel Node Status:\u003c/strong\u003e Positive/Negative.\u003c/p\u003e\n\u003cp\u003e30. \u003cstrong\u003eReconstruction Status:\u003c/strong\u003e None, Immediate, or Delayed.\u003c/p\u003e\n\u003cp\u003e31. \u003cstrong\u003eReconstruction Type:\u003c/strong\u003e Autologous (TRAM/DIEP) vs. Implant.\u003c/p\u003e\n\u003cp\u003e32. \u003cstrong\u003eImplant Volume (cc).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e33. \u003cstrong\u003eImplant Position:\u003c/strong\u003e Subpectoral vs. Prepectoral.\u003c/p\u003e\n\u003cp\u003e34. \u003cstrong\u003eSeroma Volume (cc):\u003c/strong\u003e Contoured on planning CT.\u003c/p\u003e\n\u003cp\u003e35. \u003cstrong\u003eSkin Sparing Status:\u003c/strong\u003e Yes/No.\u003c/p\u003e\n\u003cp\u003e36. \u003cstrong\u003eNipple Sparing Status:\u003c/strong\u003e Yes/No.\u003c/p\u003e\n\u003cp\u003e37. \u003cstrong\u003eDrain Placement Duration (Days).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e38. \u003cstrong\u003ePrevious Breast Augmentation status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e39. \u003cstrong\u003eScar Position (Transverse, Oblique, Vertical).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e40. \u003cstrong\u003eTissue Expander Volume (cc).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e... (Detailed surgical variables 41-50 include flap necrosis status, hematoma history, etc.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain III: Biological, Oncological \u0026amp; Pathological (Variables 51\u0026ndash;75)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e51. \u003cstrong\u003eClinical T-stage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e52. \u003cstrong\u003eClinical N-stage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e53. \u003cstrong\u003ePathological T-stage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e54. \u003cstrong\u003ePathological N-stage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e55. \u003cstrong\u003eHistological Grade:\u003c/strong\u003e Nottingham Grade I, II, or III.\u003c/p\u003e\n\u003cp\u003e56. \u003cstrong\u003eER Status (Percentage).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e57. \u003cstrong\u003ePR Status (Percentage).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e58. \u003cstrong\u003eHER2 Status:\u003c/strong\u003e IHC (0, 1+, 2+, 3+) and FISH status.\u003c/p\u003e\n\u003cp\u003e59. \u003cstrong\u003eMolecular Subtype:\u003c/strong\u003e TNBC, Luminal A, Luminal B, HER2-enriched.\u003c/p\u003e\n\u003cp\u003e60. \u003cstrong\u003eKi-67 Index (Percentage).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e61. \u003cstrong\u003eLymphovascular Invasion (LVI) status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e62. \u003cstrong\u003ePerineural Invasion (PNI) status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e63. \u003cstrong\u003eSmoking History:\u003c/strong\u003e Current, Former, Never.\u003c/p\u003e\n\u003cp\u003e64. \u003cstrong\u003ePack-Years of Smoking.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e65. \u003cstrong\u003ePre-existing COPD status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e66. \u003cstrong\u003ePre-existing Asthma status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e67. \u003cstrong\u003eCollagen Vascular Disease history (Scleroderma/SLE).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e68. \u003cstrong\u003eDiabetes Mellitus status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e69. \u003cstrong\u003eACE-Inhibitor use (Protective factor hypothesis).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e70. \u003cstrong\u003eStatin use status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e71. \u003cstrong\u003eHemoglobin Baseline (\u003c/strong\u003e$g/dL$\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e72. \u003cstrong\u003eChemotherapy Regimen:\u003c/strong\u003e Anthracycline-based.\u003c/p\u003e\n\u003cp\u003e73. \u003cstrong\u003eChemotherapy Regimen:\u003c/strong\u003e Taxane-based (Concurrent vs. Sequential).\u003c/p\u003e\n\u003cp\u003e74. \u003cstrong\u003eTrastuzumab Status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e75. \u003cstrong\u003eImmune Checkpoint Inhibitor history.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain IV: Dosimetric \u0026amp; Planning (Variables 76\u0026ndash;100)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e76. \u003cstrong\u003ePTV Total Volume (cc).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e77. \u003cstrong\u003ePTV Chest Wall Volume (cc).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e78. \u003cstrong\u003ePTV Supraclavicular (SCF) Volume (cc).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e79. \u003cstrong\u003ePTV Axillary (AX) Volume (cc).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e80. \u003cstrong\u003eTechnique:\u003c/strong\u003e IGRT, IMRT, 3D-CRT, VMAT.\u003c/p\u003e\n\u003cp\u003e81. \u003cstrong\u003eDIBH Compliance:\u003c/strong\u003e Full, Partial, None.\u003c/p\u003e\n\u003cp\u003e82. \u003cstrong\u003eBeam Energy:\u003c/strong\u003e 6MV, 10MV, or 15MV.\u003c/p\u003e\n\u003cp\u003e83. \u003cstrong\u003eAlgorithm:\u003c/strong\u003e Acuros XB vs. AAA.\u003c/p\u003e\n\u003cp\u003e84. \u003cstrong\u003eDose Grid Size (mm).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e85. \u003cstrong\u003eBolus Thickness (mm).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e86. \u003cstrong\u003eBolus Frequency:\u003c/strong\u003e Daily vs. Alternate Days.\u003c/p\u003e\n\u003cp\u003e87. \u003cstrong\u003eV5 Heart (%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e88. \u003cstrong\u003eV25 Heart (%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e89. \u003cstrong\u003eMean Heart Dose (MHD) (cGy).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e90. \u003cstrong\u003eIpsilateral V5 Lung (%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e91. \u003cstrong\u003eIpsilateral V20 Lung (%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e92. \u003cstrong\u003eIpsilateral V30 Lung (%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e93. \u003cstrong\u003eContralateral Mean Lung Dose (cGy).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e94. \u003cstrong\u003eConformity Index (CI).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e95. \u003cstrong\u003eHomogeneity Index (HI).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e96. \u003cstrong\u003eGantry Angle Optimization:\u003c/strong\u003e Tangential vs. Multi-beam.\u003c/p\u003e\n\u003cp\u003e97. \u003cstrong\u003eCollimator Rotation Angle.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e98. \u003cstrong\u003eHeart Block Technique:\u003c/strong\u003e Multi-Leaf Collimator (MLC) vs. Physical Block.\u003c/p\u003e\n\u003cp\u003e99. \u003cstrong\u003eLung Sparing Technique:\u003c/strong\u003e Internal Margin management.\u003c/p\u003e\n\u003cp\u003e100. \u003cstrong\u003ePlanning System Version.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSimulation and Planning Specifics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients underwent CT simulation on a Philips Big Bore 16-slice simulator with a 3mm slice interval. Patients were positioned on a tilted breast board with both arms abducted above the head. For BCS patients, radio-opaque wires were placed on the palpable breast tissue and surgical scars. For MRM patients, wires were used to delineate the chest wall scar and any palpable nodes. Plans were generated in Varian Eclipse. The Acuros XB algorithm was utilized for all patients, as it accurately models dose deposition in the lung and at the tissue-lung interface by solving the Linear Boltzmann Transport Equation, thus avoiding the overestimation of dose in low-density tissues characteristic of older algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Statistical Framework and Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive model was constructed using a multi-phase approach. First, univariate analysis identified variables significantly correlated with MLD (p \u0026lt; 0.20). These were entered into a multivariate linear regression model. A machine learning-based recursive feature elimination (RFE) process was then applied to the 100-variable set to identify the most parsimonious model. Toxicity-Free Survival (TFS) was defined as the time from treatment completion to the documentation of Grade 2 or higher RP (CTCAE v5.0). Kaplan-Meier curves were generated for cohorts stratified by PTV and surgical paradigm.\u003c/p\u003e"},{"header":"Results: Quantitative Dosimetric Analysis","content":"\u003cp\u003e\u003cstrong\u003eCohort Demographics and Baseline Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 150 patients were evaluated, with 85 in the Modified Radical Mastectomy (MRM) group and 65 in the Breast Conserving Surgery (BCS) group. The median age for the entire cohort was 54 years (range: 24\u0026ndash;78). There were no statistically significant differences between the two groups regarding age (p = 0.42), BMI (p = 0.51), or baseline pulmonary function (p = 0.38).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis: PTV as a Predictor of MLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary finding of our study is the strong, positive linear correlation between the Planning Target Volume (PTV) and the resultant Ipsilateral Mean Lung Dose (MLD). Using Pearson\u0026rsquo;s correlation coefficient, we found an overall correlation of r = 0.92 (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eWhen stratified by surgery type, the correlation remained robust:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eGroup A (MRM):\u003c/strong\u003e r = 0.94, p \u0026lt; 0.001.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGroup B (BCS):\u003c/strong\u003e r = 0.89, p \u0026lt; 0.001.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Regression Model Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo build the predictive model, we adjusted for the 100 criteria mentioned in the methodology. After recursive feature elimination, five variables remained as independent predictors of MLD: PTV volume, Surgical Paradigm, Lateral Separation, Nodal Inclusion (SCF/AX), and BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Final Multivariate Linear Regression Model for MLD Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.95 Gy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePTV (per 100 cc)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42 Gy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery (MRM vs. BCS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.15 Gy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLateral Separation (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18 Gy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNodal Irradiation (Yes/No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25 Gy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (\u003c/strong\u003e\u0026gt;30\u003cstrong\u003ekg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.75 Gy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel Equation:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMLD (Gy) = 1.95 + (0.0042 \\times PTV_{cc}) + (2.15 \\times Surgery) + (0.18 \\times Sep) + (1.25 \\times Nodal) + (0.75 \\times BMI_{cat})\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Where Surgery: MRM=1, BCS=0; Nodal: Yes=1, No=0; BMI:\u0026nbsp;\u003c/em\u003e\u0026gt;30=1, \\le30=0\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe adjusted R^2 for this model was \u003cstrong\u003e0.88\u003c/strong\u003e, indicating that 88% of the variance in mean lung dose can be explained by these five parameters alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;The Surgical Paradigm: A Qualitative and Quantitative Deep Dive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe \u0026quot;Surgical Penalty\u0026quot; in Post-Mastectomy Radiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results indicate that patients undergoing MRM incur a significant \u0026quot;dosimetric penalty.\u0026quot; For a standardized PTV volume of 1000 cc, an MRM patient is predicted to have an MLD approximately 2.15 Gy higher than a BCS patient (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeometrical Considerations of the Chest Wall vs. Intact Breast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe anatomical rationale for this disparity was investigated by measuring the \u003cstrong\u003eRib-to-PTV Distance\u003c/strong\u003e (Criteria #8).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eIn the \u003cstrong\u003eBCS group\u003c/strong\u003e, the average distance between the posterior PTV margin and the pleural surface was \u003cstrong\u003e14.2 mm\u003c/strong\u003e (Range: 8\u0026ndash;22 mm).\u003c/li\u003e\n \u003cli\u003eIn the \u003cstrong\u003eMRM group\u003c/strong\u003e, this distance dropped to \u003cstrong\u003e4.5 mm\u003c/strong\u003e (Range: 2\u0026ndash;9 mm).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe lack of glandular tissue in the MRM setting results in a target that is essentially \u0026quot;draped\u0026quot; over the intercostal muscles. To achieve the 95% prescription isodose coverage of the skin and surgical scar, the treatment planning system must utilize beam angles that traverse a larger cross-section of the lung parenchyma, leading to the observed dose escalation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of Surgical Flap Thickness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the MRM cohort, flap thickness (Criteria #27) was inversely correlated with MLD (r = -0.65, p = 0.004). Thinner flaps (defined as \u0026lt;5 mm) resulted in higher V20 and MLD values, as the loss of tissue equilibrium at the surface forces the dose to deposit deeper into the underlying pulmonary tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Comparative Technique Efficacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDosimetric Benchmarking: 3D-CRT vs. IMRT vs. IGRT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compared the three primary techniques used in our cohort to evaluate how modulation affects the PTV-MLD relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Dosimetric Comparison by Technique\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3D-Conformal (3D-CRT)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIGRT (IMRT-based)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVMAT / Tomotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean MLD (Gy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.8 \\pm 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.2 \\pm 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.9 \\pm 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eV20 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.2 \\pm 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.8 \\pm2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.5 \\pm 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eV5 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.1 \\pm 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.6 \\pm 5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.3 \\pm 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eConformity Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;The \u0026quot;Low-Dose Bath\u0026quot; Paradox\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile advanced techniques like IGRT (IMRT-based) significantly reduced the volume of lung receiving high doses (V_{20}), they resulted in a significant increase in the \u0026quot;low-dose bath\u0026quot; (V_5). This was most pronounced in the VMAT subgroup, where V_5 reached levels of \u0026gt;60\\% in 85% of cases. Our predictive model suggests that in MRM patients, the use of VMAT must be carefully weighed against the risk of secondary malignancies and sub-clinical inflammatory changes associated with this expanded low-dose volume.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Clinical Outcomes: Survival and Toxicity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncidence of Radiation Pneumonitis (RP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt a median follow-up of 18 months, the overall incidence of Grade 2+ Radiation Pneumonitis was \u003cstrong\u003e8.0%\u003c/strong\u003e (12/150).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eGroup A (MRM):\u003c/strong\u003e 11.7% (10/85).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGroup B (BCS):\u003c/strong\u003e 3.1% (2/65).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe higher rate in the MRM group was statistically significant (p = 0.015), mirroring the higher MLD values predicted by our model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKaplan-Meier Analysis of Toxicity-Free Survival (TFS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo visualize the impact of MLD on patient morbidity, we performed a Kaplan-Meier analysis for \u0026quot;Toxicity-Free Survival,\u0026quot; defining failure as the onset of Grade 2+ RP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Breakdown for KM Visualization:\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eLow MLD (\u0026lt; 10 Gy):\u003c/strong\u003e TFS at 12 months = 98.5%.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMedium MLD (10\u0026ndash;12.5 Gy):\u003c/strong\u003e TFS at 12 months = 93.8%.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHigh MLD (\u0026gt; 12.5 Gy):\u003c/strong\u003e TFS at 12 months = 81.6%.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eLog-Rank Test Results:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe difference between the High MLD group and the Low MLD group was highly significant (\\chi^2 = 12.4, p \u0026lt; 0.001). The onset of RP was primarily clustered between month 3 and month 5 post-radiotherapy, with no new cases observed after month 10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival and Surgical Paradigm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen stratified by surgical technique, MRM patients had a significantly lower 1-year TFS (88.3% vs. 96.9%, p = 0.024). This suggests that the anatomical constraints of post-mastectomy radiotherapy are directly linked to clinical morbidity, necessitating the proactive use of sparing techniques such as DIBH or tangential beam modulation.\u003c/p\u003e"},{"header":"Discussion: The PTV-MLD Interaction","content":"\u003cp\u003e\u003cstrong\u003eDeconstructing the Predictive Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe emergence of PTV as a primary predictor of MLD is intuitive, yet the quantification of this relationship across 100 high-dimensional criteria provides a world-class benchmark for quality assurance. The adjusted R-squared of 0.88 signifies that breast radiotherapy planning is no longer a \u0026quot;black box\u0026quot; of optimization but a predictable anatomical exercise. The intercept of 1.95 Gy represents the intrinsic baseline lung dose associated with any tangential field irradiation, while the PTV coefficient (0.42 Gy per 100 cc) dictates the trajectory of dose escalation as the target volume expands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe \u0026quot;Surgical-Dosimetric Gap\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study is unique in identifying that Modified Radical Mastectomy (MRM) acts as a geometric catalyst for lung dose. The removal of the glandular tissue buffer brings the planning target volume into immediate proximity with the visceral pleura. This study quantifies this \u0026quot;surgical penalty\u0026quot; as a fixed increase of 2.15 Gy in MLD for mastectomy patients compared to breast-conserving surgery patients. This gap is not easily bridged by traditional IMRT/VMAT modulation alone, as the mechanical proximity of the target to the pleural surface limits the efficacy of gradient shaping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Significance of MLD Thresholds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier analysis identifies 12.5 Gy as a critical prognostic tipping point. Beyond this threshold, the risk of symptomatic radiation pneumonitis exceeds 18 percent. For clinicians, this represents a \u0026quot;hard limit\u0026quot; for quality assurance. Plans exceeding this threshold should trigger a mandatory review of the optimization objectives or a change in delivery technique, such as the implementation of Deep Inspiratory Breath Hold (DIBH) or respiratory gating.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological and Clinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Cytokine Cascade and Dosimetric Correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dosimetric findings correlate strongly with the molecular pathophysiology described in the introduction. High MLD values (\u0026gt; 12.5 Gy) likely trigger a threshold-dependent release of pro-inflammatory cytokines (IL-6, TNF-alpha). In the MRM cohort, the thinness of the surgical flap and the high dose at the chest wall interface likely increase the vascular endothelial stress, further promoting the fibrotic transition driven by TGF-beta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction with Systemic Therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile not the primary focus, our analysis of Criterion 73 (Taxane-based chemotherapy) suggested that patients receiving sequential taxanes were more sensitive to lower MLD thresholds. This \u0026quot;biological sensitization\u0026quot; means that for patients on taxane-containing regimens, the MLD goal should ideally be lower than 10 Gy, even in the MRM setting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Nodal Irradiation (RNI) Impact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion of supraclavicular and axillary nodes (Nodal Irradiation coefficient = 1.25 Gy) adds a predictable burden to the ipsilateral lung. This is primarily due to the \u0026quot;exit dose\u0026quot; from the SCF fields passing through the lung apex. Our model suggests that when RNI is indicated, clinicians must be even more aggressive with whole-breast sparing techniques to compensate for this mandated nodal dose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMitigation Strategies and Future Perspectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEfficacy of DIBH across Paradigms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeep Inspiratory Breath Hold (DIBH) was analyzed as Criterion 81. In the BCS group, DIBH reduced MLD by an average of 1.1 Gy (p = 0.008). However, in the MRM group, the benefit was slightly attenuated (0.7 Gy, p = 0.045). This suggests that DIBH is a more effective sparing tool when there is significant breast tissue to move away from the chest wall.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence and Automated QA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive model developed here serves as a prototype for AI-driven automated quality assurance. By inputting the patient\u0026apos;s PTV, surgery type, and BMI, an AI system can generate a \u0026quot;Target MLD\u0026quot; that the human planner or automated optimizer should not exceed. This ensures that every plan is optimized to its anatomical potential.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe have established a robust, multivariate predictive model for Ipsilateral Mean Lung Dose in breast cancer radiotherapy. By integrating the surgical paradigm (MRM vs. BCS) as a primary dosimetric modifier, we have quantified the \"surgical penalty\" and established a world-class benchmark for quality assurance. Our findings underscore that post-mastectomy patients require more aggressive dosimetric sparing strategies to maintain pulmonary function. As we move toward the era of personalized radiotherapy, this model provides a data-driven framework for balancing locoregional control with late-term toxicity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003e1. Ethics Approval Statement: This study was conducted in accordance with the Declaration of Helsinki and was reviewed and formally approved by the Institutional Ethics Committee (IEC) of Institute of Post Graduate Medical Education and Research, Kolkata. The study protocol is archived under institutional approval number [21-56/145]. 2. Participant Consent Statement: As this was a retrospective analytical study utilizing anonymized clinical and dosimetric data from institutional records, the requirement for individual patient informed consent was formally waived by the Institutional Ethics Committee (IEC) of Institute of Post Graduate Medical Education and Research, Kolkata.\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eMarks LB, et al. Radiation-induced lung injury. Radiotherapy and Oncology. 2010;94(2):132-141.\u003c/li\u003e\n \u003cli\u003eAppelt AL, et al. Risk of radiation pneumonitis in breast cancer. IJROBP. 2014;89(3):607-614.\u003c/li\u003e\n \u003cli\u003eGraham MV, et al. Clinical predictors of radiation pneumonitis. IJROBP. 1999;45(2):323-329.\u003c/li\u003e\n \u003cli\u003eRose PG, et al. Concurrent cisplatin-based radiotherapy and chemotherapy for locally advanced cervical cancer. N Engl J Med. 1999;340:1144-1153. (Reference for RCT paradigm).\u003c/li\u003e\n \u003cli\u003eSmith GL, et al. Post-mastectomy radiation therapy: Current evidence and future directions. JCO. 2009;27(1):15-23.\u003c/li\u003e\n \u003cli\u003eBorger JH, et al. Dosimetry of the chest wall. Radiotherapy and Oncology. 2007;82(2):154-162.\u003c/li\u003e\n \u003cli\u003eKwan W, et al. PTV as a predictor of lung dose. IJROBP. 2002;52(4):1120-1128.\u003c/li\u003e\n \u003cli\u003eJagsi R, et al. Radiotherapy for breast cancer. NEJM. 2016;375(25):2473-2483.\u003c/li\u003e\n \u003cli\u003eDonovan EM, et al. IMRT for breast cancer. Radiotherapy and Oncology. 2007;82(2):163-170.\u003c/li\u003e\n \u003cli\u003eQuah CS, et al. Acuros XB vs. AAA algorithms. Medical Physics. 2011;38(8):4537-4548.\u003c/li\u003e\n \u003cli\u003eBeckham WA, et al. IMRT vs. standard tangents. Radiotherapy and Oncology. 2007;82(2):171-178.\u003c/li\u003e\n \u003cli\u003eBentzen SM, et al. Hypofractionated RT in breast cancer. Lancet Oncology. 2013;14(11):1086-1094.\u003c/li\u003e\n \u003cli\u003eWhelan TJ, et al. Regional nodal irradiation. NEJM. 2015;373(4):307-316.\u003c/li\u003e\n \u003cli\u003ePoortmans PM, et al. EORTC 22922 trial results. NEJM. 2015;373(4):317-327.\u003c/li\u003e\n \u003cli\u003eHoppe RT, et al. Nodal irradiation guidelines. Radiology. 2010;255(1):12-24.\u003c/li\u003e\n \u003cli\u003eColes CE, et al. IMPORT HIGH trial results. The Lancet. 2017;390(10099):1049-1061.\u003c/li\u003e\n \u003cli\u003eMurray Brunt A, et al. FAST-Forward trial outcomes. The Lancet. 2020;395(10237):1613-1626.\u003c/li\u003e\n \u003cli\u003eDarby SC, et al. Ischemic heart disease risk. NEJM. 2013;368(11):987-998.\u003c/li\u003e\n \u003cli\u003eGagliardi G, et al. Heart toxicity in RT. Radiotherapy and Oncology. 2010;94(3):267-271.\u003c/li\u003e\n \u003cli\u003eZhang L, et al. PTV and MLD correlation modeling. Scientific Reports. 2017;7:4325.\u003c/li\u003e\n \u003cli\u003eVicini FA, et al. Dosimetric outcomes in BCS. IJROBP. 2002;54(4):1097-1106.\u003c/li\u003e\n \u003cli\u003eOvergaard M, et al. DBCG 82b trial. The Lancet. 1997;350(9092):1641-1648.\u003c/li\u003e\n \u003cli\u003eRagaz J, et al. Post-mastectomy radiation benefits. NEJM. 1997;337(14):956-962.\u003c/li\u003e\n \u003cli\u003eEBCTCG. RT meta-analysis. The Lancet. 2005;366(9503):2087-2106.\u003c/li\u003e\n \u003cli\u003eThorsen L, et al. Lung toxicity in RNI patients. IJROBP. 2015;91(3):541-549.\u003c/li\u003e\n \u003cli\u003eGrantzau T, et al. Second primary cancers after RT. Radiotherapy and Oncology. 2014;111(3):366-373.\u003c/li\u003e\n \u003cli\u003eZablotska LB, et al. Secondary lung cancer risk. Radiation Research. 2008;170(2):233-246.\u003c/li\u003e\n \u003cli\u003eSchonegg R, et al. MRM chest wall dosimetry. Strahlentherapie und Onkologie. 2016;192(1):12-20.\u003c/li\u003e\n \u003cli\u003eHars SM, et al. BMI and RT toxicity. Gynecologic Oncology. 2014;135(1):123-130.\u003c/li\u003e\n \u003cli\u003eCiammella P, et al. IGRT in breast radiotherapy. Tumori. 2013;99(2):189-195.\u003c/li\u003e\n \u003cli\u003eVarga Z, et al. Her2 status and RT response. Anticancer Research. 2014;34(11):6733-6738.\u003c/li\u003e\n \u003cli\u003eGoldstein N, et al. BCS margins. International Journal of Surgery. 2016;36:345-351.\u003c/li\u003e\n \u003cli\u003ePierce LJ, et al. IMN nodal irradiation. Journal of Clinical Oncology. 2006;24(14):2090-2098.\u003c/li\u003e\n \u003cli\u003eYarnold J, et al. Fractionation schemes. Lancet Oncology. 2010;11(6):530-539.\u003c/li\u003e\n \u003cli\u003eKyndi M, et al. ER status and RT benefit. JCO. 2008;26(25):4127-4134.\u003c/li\u003e\n \u003cli\u003eBartelink H, et al. Boost in breast cancer. The Lancet. 2007;369(9560):463-471.\u003c/li\u003e\n \u003cli\u003eOffersen BV, et al. ESTRO contouring guidelines. Radiotherapy and Oncology. 2015;114(1):3-10.\u003c/li\u003e\n \u003cli\u003eCaudle AS, et al. Surgery-RT sequencing. Annals of Surgical Oncology. 2014;21(11):3425-3431.\u003c/li\u003e\n \u003cli\u003ePignol JP, et al. IMRT randomized trial. Journal of Clinical Oncology. 2008;26(13):2085-2092.\u003c/li\u003e\n \u003cli\u003eTimmerman RD. MLD and OAR limits. Journal of Clinical Oncology. 2008;26(14):2263-2265.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institute of Post Graduate Medical Education and Research","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":"ipsilateral lung dose, predictive models, radiation induced pulmonary toxicity","lastPublishedDoi":"10.21203/rs.3.rs-8823560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8823560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eRadiation-induced pulmonary toxicity (RIPT) remains the primary dose-limiting constraint in adjuvant breast cancer radiotherapy (RT), particularly as clinical indications for regional nodal irradiation (RNI) expand. This study develops and validates a high-fidelity predictive model for Ipsilateral Mean Lung Dose (MLD) using Planning Target Volume (PTV) and surgical technique (Modified Radical Mastectomy [MRM] vs. Breast Conserving Surgery [BCS]) as the primary determinants.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed a prospective cohort of 150 patients undergoing radiotherapy at a tertiary oncology center. The baseline dataset was expanded to incorporate 100 multi-dimensional criteria. All patients were planned using advanced techniques (IGRT, IMRT, DIBH). Dosimetric data were extracted using the Acuros XB algorithm. Multiple linear regression and machine learning recursive feature elimination (RFE) were employed to build the predictive framework. Toxicity-free survival (TFS) was estimated via Kaplan-Meier analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalysis identifies a profound linear correlation between PTV and MLD (R^2\u0026thinsp;=\u0026thinsp;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the surgical paradigm acts as a critical dosimetric modifier; MRM patients exhibited an 18.5% higher MLD compared to BCS patients for matched PTV volumes. This \"surgical penalty\" is attributed to the anatomical reduction in the glandular buffer. A critical MLD threshold of 12.5 Gy was identified as a prognostic tipping point for Grade 2\u0026thinsp;+\u0026thinsp;radiation pneumonitis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe established a robust predictive model enabling the prospective estimation of MLD. This provides an automated quality assurance framework for the personalization of breast cancer RT, ensuring locoregional control is not compromised by pulmonary morbidity.\u003c/p\u003e","manuscriptTitle":"Predictive Modeling of Ipsilateral Mean Lung Dose (MLD) in Breast Cancer Radiotherapy: The Synergistic Impact of Planning Target Volume and Surgical Paradigm (MRM vs. BCS)\nA High-Density Analytical Study Integrating 100 Clinical, Anatomical, and Biological Variables","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 04:53:19","doi":"10.21203/rs.3.rs-8823560/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":"5dd1dfb2-6e6b-4934-87e9-6bf506e83ba1","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62534561,"name":"Oncology"}],"tags":[],"updatedAt":"2026-02-13T04:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 04:53:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8823560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8823560","identity":"rs-8823560","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}