Assessment of Breast Cancer and Feasibility of Subtyping of Breast Cancer using Thoracoabdominal Staging Photon-counting Detector Computed Tomography

Assessment of Breast Cancer and Feasibility of Subtyping of Breast Cancer using Thoracoabdominal Staging Photon-counting Detector Computed Tomography | 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 Article Assessment of Breast Cancer and Feasibility of Subtyping of Breast Cancer using Thoracoabdominal Staging Photon-counting Detector Computed Tomography Claudia Neubauer, Maxim Scherwitz, Jakob Benedikt Weiß, Moisés Felipe Molina Fuentes, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7863745/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract In this prospective study, we evaluated the performance of thoracoabdominal photon-counting detector computed tomography (PCD-CT) for breast cancer assessment with MRI as reference standard and iodine uptake as a potential marker for breast cancer subtyping. 75 women (mean age: 55.8 years ± 13.9 [SD]) with 79 newly diagnosed breast cancers and indication for staging CT received a prone-positioned contrast-enhanced thoracoabdominal PCD-CT and a breast MRI. Cancer visibility and image quality (median 1/1, IQR 1/0) was rated excellent in PCD-CT on a 4-point Likert scale (1 = excellent, 4 = poor). Cancer size in PCD-CT correlated significantly with MRI ( p < 0.001). Diagnostic accuracy was good for T-stage (accuracy 0.814), focality (0.810), axillary (0.842) and internal mammary lymph nodes (0.981), moderate for ductal carcinoma in situ (0.603). A significant lower maximum iodine uptake was revealed in cancer with ductal carcinoma in situ ( p = 0.03), a significant lower mean iodine uptake in triple negative cancers ( p = 0.003). Thoracoabdominal PCD-CT demonstrated excellent cancer visibility with convincing results for assessing cancer size, T-stage, and lymph node status. Iodine uptake shows promising associations with triple negative breast cancer. Biological sciences/Cancer Health sciences/Oncology Photon-counting detector computed tomography contrast-enhanced computed tomography breast cancer staging breast cancer subtyping Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The local tumor stage of breast cancer dramatically impacts the therapeutic approach and provides crucial prognostic information [ 1 ]. Among imaging modalities, breast MRI possesses the highest sensitivity for detecting and assessing breast cancer. It outperforms clinical examination, ultrasonography, and mammography in estimating cancer size, identifying additional local disease, and detecting contralateral cancer spread [ 2 – 4 ]. This sensitivity applies for cases involving cancer associated or sole ductal carcinoma in situ (DCIS), which often manifest as non-mass enhancement [ 4 , 5 ]. In addition to breast MRI, dedicated contrast-enhanced cone-beam breast CT [ 6 , 7 ] and dedicated photon-counting detector breast CT [ 8 ] can be reliably utilized for breast diagnostics and cancer assessment. However, both breast MRI and dedicated breast CT come with additional time, costs, and the requirement for application of contrast agents. Furthermore, they are not available for all patients. In contrast to that, in our health care system and according to the national guidelines patients newly diagnosed with breast cancer undergo a thoracoabdominal CT scan for breast cancer staging purposes which already includes a scan of both breasts and the axillary region. Photon-counting detector computed tomography (PCD-CT) is an emerging technology with several advantages over dedicated breast CT. Unlike dedicated breast CT, thoracic PCD-CT can simultaneously examine and display both breasts, and additionally the thoracic wall and axillary region in the context of thoracoabdominal staging or other thoracic investigations. Due to direct conversion of photons into digital signals, the photon-counting detector technique allows for ultra-high spatial resolution, enhanced soft tissue contrast, increased iodine contrast, the ability to quantify iodine uptake, reduced artifacts, and lower radiation doses, making it a promising device for broad clinical applications [ 9 – 13 ]. In breast cancer staging, single phase contrast-enhanced thoracic PCD-CT in prone positioning has already demonstrated a higher diagnostic accuracy in assessing cancer size, T-classification, and detection DCIS compared to digital mammography [ 14 ]. Another study using a three-phase thoracic PCD-CT protocol yielded promising results in evaluating breast cancer and regional lymphadenopathy [ 15 ]. The aim of our study was to analyze the diagnostic performance of single-phase contrast-enhanced PCD-CT in prone position in the context of routine thoracoabdominal breast cancer staging compared with breast MRI, which served as the reference standard, to assess breast cancer. Our hypothesis was that thoracic PCD-CT performs comparably to breast MRI in local breast cancer assessment. Furthermore, we explored iodine uptake in breast cancer as a novel quantitative imaging marker and as a potential predictor for cancer biology. Results Study population Out of 89 eligible participants, 75 female participants (mean age: 55.8 years ± 13.9) with 79 breast cancers (bilateral cancer in 4 cases) were included in the study (Table 1 ). The exclusion criteria were incomplete imaging due to missing MRI (n = 7), examination performed with a 1.5 T MRI device (n = 3), prior breast surgery before PCD-CT and/or MRI (n = 2), histopathological confirmed DCIS without invasive carcinoma (n = 1), or male sex (n = 1) (Fig. 1 ). Cancer characteristics such as cancer entity, TNM classification (TNM eighth edition), grading, receptor status, proliferation index (Ki67), and subsequent therapy were assessed for all participants (Table 2 ). Table 1 Demographics of study participants n Number of enrolled female participants 75 Number of evaluated breast cancers 79 Unilateral left 32 (41% of cancers) Unilateral right 39 (49%) Bilateral 8 (10%) Mean age ± standard deviation 55.8 ± 13.9 Menopausal status Premenopausal 26 (35% of women) Postmenopausal 49 (65%) Breast density as diagnosed in mammography A 8 (10%) B 22 (28%) C 32 (41%) D 17 (22%) Indication for thoracic CT Staging 75 (100%) Table 2 Characteristics of assessed breast cancers n Histopathology of breast cancer No special type 41 (52%) No special type + DCIS 33 (42%) Lobular type 5 (6%) Local tumor stage, TNM T1 29 (37%) T2 28 (35%) T3 0 T4 22 (28%) Regional lymph node status cN0 48 (61%) cN1 25 (32%) cN2 3 (4%) cN3 3 (4%) Distant metastasis, TNM cM0 68 (91%) cM1 (3 oss, 1 hep, 1 oss/hep, 2 oss/hep/oth) 7 (9%) Grading Grade 1 3 (4%) Grade 2 52 (66%) Grade 3 24 (30%) ER Negative 15 (19%) Positive 64 (81%) PR Negative 24 (30%) Positive 55 (70%) HER2 Negative 63 (80%) Positive 16 (20%) Proliferation index (Ki67) 10% 77 (97%) Neoadjuvant chemotherapy No 35 (44%) Yes 40 (51%) Palliative 4 (5%) Total n = 79 tumors, except for metastasis total n = 75 patients Breast cancer visibility, image quality, and confidence in diagnostic assessment in PCD-CT Cancer visibility was rated as very good in monoenergetic 65 keV reconstructions (median of 1/1/1 and IQR of 0/1/1 for raters 1, 2, 3, respectively), good to very good in the iodine map (median 1/1/2 and IQR 0/1/1.5), and moderate in the virtual non-contrast reconstruction (median 3/3/3 and IQR 2/2/2). Ratings for cancer visibility in monoenergetic 65 keV reconstructions and iodine maps with PCD-CT were non-inferior to ratings of cancer visibility obtained with the reference standard MRI (for each rater p < 0.001). The overall image quality of PCD-CT was rated as very good by all raters (median of 1/1/1, IQR 0/0/0). The overall confidence in diagnostic assessment was rated as very good with PCD-CT (median of 1/1/1, IQR 1/1/0), and non-inferior to confidence in diagnostic assessment with MRI (for each rater p < 0.001). Figure 2 provides a representative example of bilateral breast cancer and axillary lymph node metastasis. Breast cancer and lymph node assessment in PCD-CT Average correlation between all measurements of largest cancer diameter in monoenergetic 65 keV reconstructions and iodine maps in PCD-CT was excellent (Fig. 3 ). When comparing measurements, excluding diffuse and hardly measurable cancer expansion greater than 5 cm, with the reference standard MRI (n = 73), correlation and agreement were excellent for both monoenergetic 65 keV reconstructions (Fig. 4 , upper panel) and iodine maps (Fig. 4 , lower panel) for all raters. The diagnostic accuracy for T-classification (TNM eighth edition) was altogether 0.814 (average of 0.873/0.835/0.734 for rater 1, 2, 3, respectively), for focality 0.810 (average of 0.810/0.835/0.785). For suspicious axillary lymph nodes, the diagnostic accuracy was 0.842 and for internal mammary lymph nodes 0.981 (average of rater 1 and 2, who evaluated lymph nodes). Sensitivity and specificity for depiction of suspicious lymph nodes in PCD-CT with reference to MRI was 0.83 and 0.82 for axillary lymph nodes and 0.92 and 0.99 for internal mammary lymph nodes. Due to neoadjuvant chemotherapy, histopathological results for cancer extent were available in only 35 cases. When comparing the diagnostic assessment from all raters to those histopathological cancer stages, the diagnostic accuracy for T-stage assessment using PCD-CT was 0.781. Comparison of evaluations of axillary lymph nodes to histopathological findings (from initial biopsy or primary surgery, n = 50 cases), yielded a diagnostic accuracy of 0.800, with a sensitivity of 0.76 and a specificity of 0.85 for PCD-CT. DCIS was depicted with a diagnostic accuracy of altogether 0.603 (0.582/ 0.671/0.557 for rater 1,2,3, respectively) in all cases in comparison to the reference standard of MRI. When compared to histopathological findings, the average diagnostic accuracy of all raters for DCIS was 0.586 with a sensitivity of 0.38 and a specificity of 0.73 for PCD-CT. Interrater reliability Interrater reliability with intra-class correlation coefficient showed a good agreement for cancer size measurement in the monoenergetic 65 keV reconstructions (kappa = 0.841, p < 0.001) and the iodine map (0.841, p < 0.001), and a moderate agreement in the virtual non-contrast reconstruction (0.784, p < 0.001). Interrater agreement was substantial for T-classification (0.707, p < 0.001), dermal infiltration (0.680, p < 0.001), focality (0.711, p 10 lymph nodes (0.629, p < 0.001 and 0.634, p < 0.001), and for suspicious internal mammary lymph nodes (0.749, p < 0.001). Interrater agreement was moderate for number of lesions (0.545, p < 0.001), infiltration of pectoral muscles with 0.474 ( p < 0.001), and only fair for the presence of DCIS suspicious findings (0.220, p < 0.001). Iodine uptake in breast cancer with molecular subtyping Enhancement, as quantified by iodine uptake using the iodine map, showed a significant difference between cancer entities. For measured maximum iodine uptake, the average and highest iodine values were 3.514 mg/mL (SD 0.79) and 4.961 mg/mL in NST cancers, 2.992 mg/mL (SD 0.74) and 4.538 mg/mL in NST cancers associated with DCIS, and 3.015 mg/mL (SD 1.44) and 5.461 mg/mL in lobular cancers ( p = 0.02). The post-hoc Dunn analysis revealed a significant difference between NST cancers and NST cancers with associated DCIS ( p = 0.03) (Fig. 5 A). However, no significant difference was observed in the measured mean iodine uptake with an average and highest value of 1.808 mg/mL (SD 0.69) and 3.192 mg/mL in NST cancers, of 1.763 mg/mL (SD 0.65) and 3.192 mg/mL in NST cancers with associated DCIS, and of 1.315 mg/mL (SD 1.02) and 2.885 mg/mL in lobular cancers ( p = 0.32) (Fig. 5 B). We found no significant difference in measured maximum iodine uptake regarding estrogen or progesterone receptor status and HER2 status (p = 0.98/0.42/0.39), between triple negative and all other cancer subtypes ( p = 0.51), and altogether between all cancer subtypes (Luminal A, Luminal B HER2+, Luminal B HER2-, HER2 type and triple negative type) ( p = 0.71, see Figure, Supplemental Digital Content 1, which illustrates these results). In contrast, in breast cancer with negative estrogen or progesterone receptor status, a significant lower measured mean iodine uptake was noted compared to cancer with positive receptor status ( p = 0.03 and p = 0.01, Fig. 6 ). Although there was no significant difference in measured mean iodine uptake for HER2 status ( p = 0.46), triple negative breast cancer demonstrated significantly lower measured mean iodine uptake compared to all other breast cancer subtypes, both collectively and separately ( p = 0.003 and p = 0.04, Fig. 6 ). Additionally, there was also no difference in measured maximum or mean iodine uptake across the three cancer grades ( p = 0.71 and p = 0.24, see Figure, Supplemental Digital Content 2, which further illustrates these results). The cancer-to-aorta ratios for measured maximum and mean HU measurements in the monoenergetic 65 keV reconstructions also showed no significant differences when comparing cancer types, grades, receptor status (in all cases p > 0.05). Moreover, central necrosis was detected by MRI in 15/79 cancers and appeared to have a significant prevalence in triple negative cancer in both modalities, MRI ( p = 0.005) and PCD-CT (average p < 0.001) with an intermediate effect size (Cohen's ω = 0.372 for MRI, 0.339 for PCD-CT). Discussion In our study, we evaluated the diagnostic performance of thoracic contrast-enhanced PCD- CT in prone position, using breast MRI as the reference standard for breast cancer assessment. Cancer visibility and image quality were rated as very good and non-inferior to MRI ( p < 0.001), with PCD-CT showing comparable performance to MRI for breast cancer size measurements ( p < 0.001). Diagnostic accuracy was good for T-classification (0.814) and axillary lymph node (0.842) and very good for internal mammary lymph node status (0.981). When compared to histopathological findings after surgery, PCD-CT demonstrated good diagnostic accuracy for T-stage (0.781) and axillary lymph node assessment (0.800). However, the accuracy for detecting DCIS as non-mass enhancement was only moderate, both compared to MRI (0.603) and histopathology (0.586). Quantification of iodine uptake in breast carcinomas revealed a significant difference in maximum iodine uptake between NST cancer and NST cancer with associated DCIS ( p = 0.03) as well as in mean iodine uptake for positive versus negative estrogen and progesterone receptor status ( p = 0.03 and 0.01) and triple negative versus non-triple negative breast cancers ( p = 0.003). These findings align with previous studies that demonstrated a higher diagnostic accuracy for breast carcinoma size assessment and T-stage classification using thoracic PCD-CT in prone position compared to digital mammography [ 14 ], as well as promising results in assessing breast cancer and regional lymphadenopathy using three-phase thoracic PCD-CT [ 15 ]. We opted for a one-phase protocol, as has been utilized in prior PCD-CT and dual-source CT studies [ 14 , 18 – 20 ], and which demonstrated a high agreement between monoenergetic reconstructions and iodine maps for carcinoma measurements, consistent with prior results from dual energy CT [ 21 ]. In terms of DCIS detection, previous studies reported a better performance of thoracic PCD-CT compared to digital mammography [ 14 ], and a significant correlation between dedicated breast photon-counting CT and histopathology [ 22 ]. In our study, the diagnostic accuracy of PCD-CT for detecting DCIS as non-mass enhancement was only moderate compared to MRI (0.603) and histopathological findings (0.586), which may be attributed to generally lower enhancement and less defined spread of DCIS. Despite of a particularly low sensitivity, the specificity of detecting DCIS was good (0.73), comparable to other studies using multidetector row CT [ 23 – 26 ]. We also observed a significantly lower mean iodine uptake in NST tumors that were accompanied by DCIS, which may possibly be attributed to cases of less defined transition of solid carcinoma into low enhancing DCIS. Anyway, PCD-CT with its high spatial resolution [ 27 ] may offer greater potential for assessing DCIS when additionally evaluating the detectability of suspicious microcalcifications as has already been explored in a breast phantom study for thoracic PCD-CT [ 28 ]. Also, the capability of differentiating between cancer subtypes such as triple negative and non-triple negative cancer based on iodine uptake as shown in this study aligns with previous research. Evaluations of contrast-enhanced chest CT have indicated a correlation between iodine content and hormone receptor expression [ 29 ] as well as a capability to distinguish between different cancer types, particularly triple negative and non-triple negative cancer [ 30 , 31 ]. Furthermore, morphological tumor features have been shown to be helpful in differentiating breast cancer subtypes, receptor status, or aggressiveness with dedicated breast CT [ 32 ] and breast MRI [ 33 , 34 ]. For example, triple negative cancer has been associated with rim enhancement [ 33 , 34 ] or intratumoral heterogeneity in MRI [ 35 ] or correspondingly with higher HU differences within the tumor in CT [ 32 ]. Limitations of our study are the single-center design and the limited number of participants and therefore the limited and unbalanced number of tumor entities. Second, we only included participants with indications for thoracic CT, which, however, is regularly performed for initial staging in breast cancer participants in our health care system. Third, comparison to histopathology after cancer surgery was not possible in all cases due to neoadjuvant or palliative treatment for some participants, anyway, the main aim of the study was a comparison to MRI as the reference standard for imaging in breast cancer assessment. Still, we added the comparison regarding DCIS and lymph node status in cases of initial surgical resection to add more certainty to the status than could be provided by MRI alone. Finally, tumor segmentation and assessment of iodine uptake was only performed by one reader. In conclusion, thoracic contrast-enhanced PCD-CT provided excellent cancer visibility and demonstrated a very good performance in comparison to MRI for assessing breast cancer size, T-stage, and lymph nodes. Iodine uptake measurements suggest that PCD-CT may be capable of distinguishing between cancer subtypes, such as NST cancer and NST cancer with associated DCIS and identifying triple negative breast cancer. Additionally, in contrast to other breast-specific imaging modalities, thoracic PCD-CT provides a comprehensive staging of breast cancer, including an assessment of the M-stage, all within a single examination. Further studies are required to validate the efficacy and quality of thoracic PCD-CT in local breast cancer assessment and to explore its potential in cancer differentiation and molecular subtyping. Materials and Methods Study design and population This prospective study was conducted at a tertiary university medical center from December 2021 to August 2023. All participants with newly diagnosed biopsy-proven breast cancer, who had a current mammography, a medical indication for a thoracoabdominal contrast-enhanced CT in the context of breast cancer staging, and no contraindications for breast MRI were included after written informed consent. Pregnant or breast-feeding women were primarily not included in the study. Exclusion criteria were incomplete or no 3T MRI imaging, imaging after surgery, no invasive cancer, or male patients (compare Fig. 1 ). The present study including the protocols was approved by our institutional review board and in accordance with national guidelines and the Declaration of Helsinki. There are no conflicts of interest. Thoracic photon-counting detector CT examination and reconstructions Dual source photon-counting detector CT (NAEOTOM-Alpha; Siemens Healthineers, Germany) examinations were performed in prone position with both breasts hanging freely between pillows comparable to positioning in breast MRI. Our protocol included a single-phase helical acquisition at 120 kVp and a quality reference of 142 mAs after bodyweight–adapted injection of iopromide (370 mg/mL; Bayer, Germany), followed by a saline chaser with a flow of 3 ml/s and a fixed delay of 85 seconds. For evaluation of breasts and axillary lymph node status we added transversal reconstructions with a field of view equivalent to transversal breast MRI scans. These reconstructions were performed at a monoenergetic level of 65 keV, as an iodine map, and a virtual non-contrast reconstruction covering both breasts and the anterior thoracic wall including the axillary region with a fixed field of view of 34 cm, a matrix of 1024x1024 pixels, a slice thickness and increment of 2 mm, kernel Br40, and iterative reconstruction strength 3. Breast MRI examination All participants underwent a multiparametric 3T breast MRI (MAGNETOM Vida; Siemens Healthineers, Germany) within a period of 0 to 17 days (mean 2.29 days, ± 6.1 SD) following the PCD-CT examination. Breast MRI was performed with an 18-channel breast coil after application of 0.1 mmol/kg contrast agent (Gatoteridol, ProHance, Bracco, Konstanz, Germany). The MRI protocol consisted of a transversal T2 dixon vibe (including in-phase and water only image series), transversal diffusion-weighted imaging, always transversal native followed by four dynamic contrast-enhanced T1-weighted dixon vibe sequences every 90 seconds over 360 seconds altogether with generation of maximum intensity projections as well as subtracted images and additionally ultrafast dynamic breast imaging using TWIST sequences (missing in one case) during contrast agent application. All sequences were used for the evaluation of MRI as reference standard regarding imaging parameters. Image analysis and reference standard The evaluation of pseudonymized PCD-CT examinations was performed by three different experienced radiologists (radiologist 1: 12 years CT imaging and 7 years breast imaging, radiologist 2: 10 years CT imaging and 6 months sole breast imaging, radiologist 3: 6 years CT imaging and 5 months sole breast imaging). Raters were informed about the side of breast malignancy. Otherwise, these raters were blinded to patient data. The three raters independently evaluated cancer visibility in all reconstructions, overall confidence in diagnostic assessment, and overall image quality of PCD-CT and MRI imaging on a 4-point Likert scale (1 = excellent; 4 = poor). All raters measured the largest tumor diameter in all reconstructions, assessed tumor focality (unifocal versus multifocal/multicentric tumor), skin or pectoralis/thoracic wall infiltration, DCIS suspicious findings (non-mass enhancement). Axillary and internal mammary lymph nodes were assessed by the two most experienced readers and were rated as suspicious when they appeared with a marked asymmetry, a cortical thickening, loss of the fatty hilum, a round and enlarged shape, an irregular margin, a heterogeneous cortex, and/or surrounding edema. Rater 1 also measured the mean and maximum Hounsfield Units (HU) of main tumors in monoenergetic 65 keV reconstructions and iodine maps using a ROI, that was created in the most representative axial slice with the maximum tumor diameter and by manually surrounding the whole solid tumor on that slice for analysis. In case of multifocality or multicentricity the main solid tumor was chosen for this segmentation. Another ROI was positioned in the ascending aorta to assess the relative contrast in cancer versus aortic contrast with mean and maximum HU in monoenergetic 65 keV reconstructions. From mean and maximum HU values in the iodine map, the iodine uptake value in mg/mL was calculated. The reference standard (MRI) was evaluated by two experienced radiologists in consensus blinded to ratings from and independent from PCD-CT (radiologist with more than 25 years of CT and breast imaging and another radiologist with 14 years CT imaging and 7 years breast imaging). Regarding presence of DCIS and axillary lymph node assessment, histopathology was considered as reference standard in cases without neoadjuvant treatment and/or prior lymph node biopsy to additionally assess sensitivity and specificity in those cases with definitive histopathology. Information about histologic cancer type and grade, hormone receptor and HER2 status, and Ki-67 index were provided in the histopathological reports. All other clinical data were collected from the medical records. Statistics For descriptive statistics continues variables are reported as mean ± SD, whereas categorical variables are reported as counts and percentages. Likert-scale ratings of cancer visibility, confidence in diagnostic assessment and image quality are reported as median with interquartile range (IQR), and comparison with reference standard MRI was evaluated by paired non-inferiority tests, which are a variant of a t-test with exchanged hypotheses. Correlations between cancer diameter measurements of PCD-CT reconstructions (monoenergetic 65 keV reconstruction versus iodine map) and with reference standard MRI were visualized by scatter plots and quantified by variance explained (adjusted R2) from linear regression. Agreement between PCD-CT and reference standard MRI was assessed by Bland-Altman plots. Diagnostic accuracy was calculated for PCD-CT with MRI as the reference standard regarding correct T-stage classification, focality, and suspicious axillary and internal mammary lymph nodes. Additionally, in cases of histopathological assessment of lymph nodes, sensitivity and specificity were calculated for PCD-CT with histopathology as the reference standard for lymph node status. The same was performed for non-mass enhancement as a DCIS suspicious finding in case of histopathological proven cancer associated DCIS or cancer surgery. Agreement between PCD-CT and MRI regarding cancer type (triple negative versus non-triple negative) and cancer necrosis was tested with Fisher’s or Chi-square test and Cohen's ω for effect size. Distribution of average and maximum cancer-to-aorta HU values and iodine uptake values for different histopathological cancer entities was visualized by violin plots and differences were evaluated using the Kruskal-Wallis test by ranks with post-hoc Dunn tests. Comparisons of HU values and iodine uptake for ER status, PR status, HER2 status, or between triple negative versus all other cancer entities together was performed with Mann-Whitney U test, between triple negative versus all other cancer entities separately with the Kruskal-Wallis test by ranks with post-hoc Dunn tests. Interrater reliability was calculated using Fleiss’ kappa for nominal values and intra-class correlation with a two-way-random effects model and a single-rater unit for cancer size measurements. For Fleiss’ kappa κ , cutoffs of 0.2, 0.4, 0.6, 0.8, and 1 were considered to denote light, fair, moderate, substantial, and almost perfect agreement, respectively [ 16 ]. For intra-class correlation a value less than 0.5 indicated poor reliability, values between 0.5 and 0.75 moderate, between 0.75 and 0.9 good, and greater than 0.90 excellent reliability [ 17 ]. P values < 0.05 were considered to indicate statistical significance. We used R version 4.3.2 for all statistical analysis. Abbreviations DCIS ductal carcinoma in situ ER estrogen receptor HER2 human epidermal growth factor receptor 2 IQR interquartile range NST breast cancer of no special type PCD-CT photon-counting detector computed tomography PR progesterone receptor Declarations Data Sharing Statement Data generated or analyzed during the study are available from the corresponding author by request. Competing Interests Statement None. Funding Only internal funding. Author Contribution All the authors contributed to the various stages of the study and the manuscript preparation.CN and JN developed the concept for this study. FAT, IJB, JN, LJ, MS, OG and SH acquired the patients. FB and MWB provided the devices and the infrastructure. CN, MS and OG prepared the data and MS the image analysis. CN, JN, JW, MMF and MWB performed the image analysis. CN analyzed the data and performed the statistical analysis. CN created the tables and figures. CN wrote the manuscript. All authors read and reviewed the manuscript and approved the final manuscript. Acknowledgement All authors would like to express their sincere thanks to Dr. Susanne Rospleszcz for the statistical advice in this study. Data Availability Data generated or analyzed during the study are available from the corresponding author by request. References Burstein, H. J. et al. 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Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging. Eur. Radiol. 32 , 822–833 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementalDigitalContent1and2.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Editor invited by journal 30 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 29 Oct, 2025 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-7863745","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539442077,"identity":"7d586696-ca9b-4162-8dab-91468c8beac1","order_by":0,"name":"Claudia 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08:49:34","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":101616,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/f83eb287830f8c8beb955dc2.html"},{"id":95808588,"identity":"5f4dae01-e063-4260-a62e-ffb4c8c857a5","added_by":"auto","created_at":"2025-11-13 08:49:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180398,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for eligibility, exclusion and final collective of participants.\u003c/p\u003e\n\u003cp\u003eDCIS ductal carcinoma in situ, PCD-CT photon-counting detector computed tomography, MRI magnetic resonance imaging\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/6d63e025b27e2ab90f32cbcf.png"},{"id":95808369,"identity":"99473d8e-a6c8-4425-80a5-e2f874d1f4a6","added_by":"auto","created_at":"2025-11-13 08:49:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":354492,"visible":true,"origin":"","legend":"\u003cp\u003e61-year-old participant with bilateral breast cancer of no special type in monoenergetic 65 keV reconstruction (A) and iodine map (B) of contrast-enhanced thoracic photon-counting detector computed tomography, and subtraction imaging of contrast-enhanced MRI (C). On the right side the cancer is clearly detectable but, in all modalities, difficult to measure exactly due to diffuse multicentric expansion with dermal and pectoral muscle invasion (thick short arrows) and is accompanied by axillary lymph node metastasis (arrowhead). On the left side there is a small clearly depictable and exactly measurable cancer (thin long arrow).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/3fbc0518f426d1a529cec1d0.png"},{"id":95808397,"identity":"b837436a-bbbf-4e79-9b79-ffacc0b74b21","added_by":"auto","created_at":"2025-11-13 08:49:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102280,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of measurements of largest cancer diameter in the monoenergetic 65 keV reconstructions (x-axis) and iodine map (y-axis) in thoracic contrast-enhanced photon-counting detector computed tomography.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/eb3011438e89fa5f26d14003.png"},{"id":95808405,"identity":"f4661a1e-d7d5-4bdd-a8ac-d5fcf660fa38","added_by":"auto","created_at":"2025-11-13 08:49:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":755135,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation (scatter plots) and agreement (Bland-Altman plots) between cancer diameter measurements in monoenergetic 65 keV reconstructions (A, above) and the iodine map (B, below) in thoracic contrast-enhanced photon-counting detector computed tomography versus the reference diameter as measures in breast MRI for all raters.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/38e6cbb8c8d620df33f3687c.png"},{"id":95808474,"identity":"83f2af68-aa14-4728-9bef-bca0cfad2767","added_by":"auto","created_at":"2025-11-13 08:49:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188687,"visible":true,"origin":"","legend":"\u003cp\u003eMeasured maximum (A) and mean (B) iodine uptake in mg/mL for no special type (NST), no special type associated with DCIS (NST+DCIS) and lobular type of breast cancer. A significant difference was shown for maximum iodine uptake (\u003cem\u003ep \u003c/em\u003e= 0.017) between NST and NST+DCIS (\u003cem\u003ep\u003c/em\u003e = 0.027) (B).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/cca682f4c3af04d96e1650ca.png"},{"id":95808806,"identity":"dfeb14ad-3d2a-49a6-a38c-f1538b024c19","added_by":"auto","created_at":"2025-11-13 08:49:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":539584,"visible":true,"origin":"","legend":"\u003cp\u003eMean iodine uptake in mg/mL for estrogen receptor status (A), progesterone receptor status (B), HER2 status (c), triple negative versus non-triple negative cancer (D), and all cancer entities (E). A significant difference was shown for mean iodine uptake with lower uptake in estrogen receptor negative and progesterone receptor negative breast cancer (\u003cem\u003ep\u003c/em\u003e = 0.031 and 0.014) as well as in triple negative breast versus non-triple negative cancers (\u003cem\u003ep\u003c/em\u003e = 0.003) and versus Luminal B (HER2-) cancer when differentiating all cancer types separately (\u003cem\u003ep\u003c/em\u003e = 0.045), although all cancers showed a higher mean iodine uptake than triple negative carcinomas (E). No significant difference was observed in mean iodine uptake between HER2 negative and positive breast cancer (C). Corresponding figures for maximum iodine uptake are shown in the Figure, Supplemental Digital Content 1.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/05f6666a54cc7e38a85bc281.png"},{"id":95810624,"identity":"22f2f840-a1b9-45db-bb33-942bb314935a","added_by":"auto","created_at":"2025-11-13 08:53:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2915755,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/a5515c93-3b54-4b43-a771-908318057037.pdf"},{"id":95808498,"identity":"9085ee5a-7200-4165-bb30-70a473c0f3ca","added_by":"auto","created_at":"2025-11-13 08:49:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8397566,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalDigitalContent1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7863745/v1/afc8cd6dcd81d17cdd90638d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Breast Cancer and Feasibility of Subtyping of Breast Cancer using Thoracoabdominal Staging Photon-counting Detector Computed Tomography","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe local tumor stage of breast cancer dramatically impacts the therapeutic approach and provides crucial prognostic information [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among imaging modalities, breast MRI possesses the highest sensitivity for detecting and assessing breast cancer. It outperforms clinical examination, ultrasonography, and mammography in estimating cancer size, identifying additional local disease, and detecting contralateral cancer spread [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This sensitivity applies for cases involving cancer associated or sole ductal carcinoma in situ (DCIS), which often manifest as non-mass enhancement [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition to breast MRI, dedicated contrast-enhanced cone-beam breast CT [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and dedicated photon-counting detector breast CT [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] can be reliably utilized for breast diagnostics and cancer assessment. However, both breast MRI and dedicated breast CT come with additional time, costs, and the requirement for application of contrast agents. Furthermore, they are not available for all patients. In contrast to that, in our health care system and according to the national guidelines patients newly diagnosed with breast cancer undergo a thoracoabdominal CT scan for breast cancer staging purposes which already includes a scan of both breasts and the axillary region.\u003c/p\u003e\u003cp\u003ePhoton-counting detector computed tomography (PCD-CT) is an emerging technology with several advantages over dedicated breast CT. Unlike dedicated breast CT, thoracic PCD-CT can simultaneously examine and display both breasts, and additionally the thoracic wall and axillary region in the context of thoracoabdominal staging or other thoracic investigations. Due to direct conversion of photons into digital signals, the photon-counting detector technique allows for ultra-high spatial resolution, enhanced soft tissue contrast, increased iodine contrast, the ability to quantify iodine uptake, reduced artifacts, and lower radiation doses, making it a promising device for broad clinical applications [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In breast cancer staging, single phase contrast-enhanced thoracic PCD-CT in prone positioning has already demonstrated a higher diagnostic accuracy in assessing cancer size, T-classification, and detection DCIS compared to digital mammography [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another study using a three-phase thoracic PCD-CT protocol yielded promising results in evaluating breast cancer and regional lymphadenopathy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aim of our study was to analyze the diagnostic performance of single-phase contrast-enhanced PCD-CT in prone position in the context of routine thoracoabdominal breast cancer staging compared with breast MRI, which served as the reference standard, to assess breast cancer. Our hypothesis was that thoracic PCD-CT performs comparably to breast MRI in local breast cancer assessment. Furthermore, we explored iodine uptake in breast cancer as a novel quantitative imaging marker and as a potential predictor for cancer biology.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eOut of 89 eligible participants, 75 female participants (mean age: 55.8 years\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9) with 79 breast cancers (bilateral cancer in 4 cases) were included in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The exclusion criteria were incomplete imaging due to missing MRI (n\u0026thinsp;=\u0026thinsp;7), examination performed with a 1.5 T MRI device (n\u0026thinsp;=\u0026thinsp;3), prior breast surgery before PCD-CT and/or MRI (n\u0026thinsp;=\u0026thinsp;2), histopathological confirmed DCIS without invasive carcinoma (n\u0026thinsp;=\u0026thinsp;1), or male sex (n\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cancer characteristics such as cancer entity, TNM classification (TNM eighth edition), grading, receptor status, proliferation index (Ki67), and subsequent therapy were assessed for all participants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eDemographics of study participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of enrolled female participants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of evaluated breast cancers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnilateral left\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (41% of cancers)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnilateral right\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (49%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean age\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenopausal status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (35% of women)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (65%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreast density as diagnosed in mammography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (28%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (41%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (22%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndication for thoracic CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStaging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (100%)\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\u003e\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\u003eCharacteristics of assessed breast cancers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistopathology of breast cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo special type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (52%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo special type\u0026thinsp;+\u0026thinsp;DCIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (42%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobular type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocal tumor stage, TNM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (37%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (35%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (28%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional lymph node status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (61%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (32%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistant metastasis, TNM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (91%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecM1 (3 oss, 1 hep, 1 oss/hep, 2 oss/hep/oth)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (66%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (19%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (81%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (70%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (80%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (20%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProliferation index (Ki67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (97%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoadjuvant chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (51%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalliative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eTotal n\u0026thinsp;=\u0026thinsp;79 tumors, except for metastasis total n\u0026thinsp;=\u0026thinsp;75 patients\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBreast cancer visibility, image quality, and confidence in diagnostic assessment in PCD-CT\u003c/h3\u003e\n\u003cp\u003eCancer visibility was rated as very good in monoenergetic 65 keV reconstructions (median of 1/1/1 and IQR of 0/1/1 for raters 1, 2, 3, respectively), good to very good in the iodine map (median 1/1/2 and IQR 0/1/1.5), and moderate in the virtual non-contrast reconstruction (median 3/3/3 and IQR 2/2/2). Ratings for cancer visibility in monoenergetic 65 keV reconstructions and iodine maps with PCD-CT were non-inferior to ratings of cancer visibility obtained with the reference standard MRI (for each rater \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall image quality of PCD-CT was rated as very good by all raters (median of 1/1/1, IQR 0/0/0). The overall confidence in diagnostic assessment was rated as very good with PCD-CT (median of 1/1/1, IQR 1/1/0), and non-inferior to confidence in diagnostic assessment with MRI (for each rater \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a representative example of bilateral breast cancer and axillary lymph node metastasis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eBreast cancer and lymph node assessment in PCD-CT\u003c/h3\u003e\n\u003cp\u003eAverage correlation between all measurements of largest cancer diameter in monoenergetic 65 keV reconstructions and iodine maps in PCD-CT was excellent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When comparing measurements, excluding diffuse and hardly measurable cancer expansion greater than 5 cm, with the reference standard MRI (n\u0026thinsp;=\u0026thinsp;73), correlation and agreement were excellent for both monoenergetic 65 keV reconstructions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, upper panel) and iodine maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, lower panel) for all raters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic accuracy for T-classification (TNM eighth edition) was altogether 0.814 (average of 0.873/0.835/0.734 for rater 1, 2, 3, respectively), for focality 0.810 (average of 0.810/0.835/0.785). For suspicious axillary lymph nodes, the diagnostic accuracy was 0.842 and for internal mammary lymph nodes 0.981 (average of rater 1 and 2, who evaluated lymph nodes). Sensitivity and specificity for depiction of suspicious lymph nodes in PCD-CT with reference to MRI was 0.83 and 0.82 for axillary lymph nodes and 0.92 and 0.99 for internal mammary lymph nodes.\u003c/p\u003e\u003cp\u003eDue to neoadjuvant chemotherapy, histopathological results for cancer extent were available in only 35 cases. When comparing the diagnostic assessment from all raters to those histopathological cancer stages, the diagnostic accuracy for T-stage assessment using PCD-CT was 0.781. Comparison of evaluations of axillary lymph nodes to histopathological findings (from initial biopsy or primary surgery, n\u0026thinsp;=\u0026thinsp;50 cases), yielded a diagnostic accuracy of 0.800, with a sensitivity of 0.76 and a specificity of 0.85 for PCD-CT.\u003c/p\u003e\u003cp\u003eDCIS was depicted with a diagnostic accuracy of altogether 0.603 (0.582/ 0.671/0.557 for rater 1,2,3, respectively) in all cases in comparison to the reference standard of MRI. When compared to histopathological findings, the average diagnostic accuracy of all raters for DCIS was 0.586 with a sensitivity of 0.38 and a specificity of 0.73 for PCD-CT.\u003c/p\u003e\n\u003ch3\u003eInterrater reliability\u003c/h3\u003e\n\u003cp\u003eInterrater reliability with intra-class correlation coefficient showed a good agreement for cancer size measurement in the monoenergetic 65 keV reconstructions (kappa\u0026thinsp;=\u0026thinsp;0.841, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the iodine map (0.841, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a moderate agreement in the virtual non-contrast reconstruction (0.784, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eInterrater agreement was substantial for T-classification (0.707, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), dermal infiltration (0.680, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), focality (0.711, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suspicious axillary lymph nodes altogether as well as grouped according to the pathologic N classification with 0, 1\u0026ndash;3, 4\u0026ndash;9, \u0026gt;\u0026thinsp;10 lymph nodes (0.629, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 0.634, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and for suspicious internal mammary lymph nodes (0.749, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interrater agreement was moderate for number of lesions (0.545, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), infiltration of pectoral muscles with 0.474 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and only fair for the presence of DCIS suspicious findings (0.220, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eIodine uptake in breast cancer with molecular subtyping\u003c/h3\u003e\n\u003cp\u003eEnhancement, as quantified by iodine uptake using the iodine map, showed a significant difference between cancer entities. For measured maximum iodine uptake, the average and highest iodine values were 3.514 mg/mL (SD 0.79) and 4.961 mg/mL in NST cancers, 2.992 mg/mL (SD 0.74) and 4.538 mg/mL in NST cancers associated with DCIS, and 3.015 mg/mL (SD 1.44) and 5.461 mg/mL in lobular cancers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). The post-hoc Dunn analysis revealed a significant difference between NST cancers and NST cancers with associated DCIS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). However, no significant difference was observed in the measured mean iodine uptake with an average and highest value of 1.808 mg/mL (SD 0.69) and 3.192 mg/mL in NST cancers, of 1.763 mg/mL (SD 0.65) and 3.192 mg/mL in NST cancers with associated DCIS, and of 1.315 mg/mL (SD 1.02) and 2.885 mg/mL in lobular cancers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe found no significant difference in measured maximum iodine uptake regarding estrogen or progesterone receptor status and HER2 status (p\u0026thinsp;=\u0026thinsp;0.98/0.42/0.39), between triple negative and all other cancer subtypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51), and altogether between all cancer subtypes (Luminal A, Luminal B HER2+, Luminal B HER2-, HER2 type and triple negative type) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71, see Figure, Supplemental Digital Content 1, which illustrates these results). In contrast, in breast cancer with negative estrogen or progesterone receptor status, a significant lower measured mean iodine uptake was noted compared to cancer with positive receptor status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Although there was no significant difference in measured mean iodine uptake for HER2 status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46), triple negative breast cancer demonstrated significantly lower measured mean iodine uptake compared to all other breast cancer subtypes, both collectively and separately (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Additionally, there was also no difference in measured maximum or mean iodine uptake across the three cancer grades (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24, see Figure, Supplemental Digital Content 2, which further illustrates these results).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe cancer-to-aorta ratios for measured maximum and mean HU measurements in the monoenergetic 65 keV reconstructions also showed no significant differences when comparing cancer types, grades, receptor status (in all cases \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eMoreover, central necrosis was detected by MRI in 15/79 cancers and appeared to have a significant prevalence in triple negative cancer in both modalities, MRI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and PCD-CT (average \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with an intermediate effect size (Cohen's ω\u0026thinsp;=\u0026thinsp;0.372 for MRI, 0.339 for PCD-CT).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we evaluated the diagnostic performance of thoracic contrast-enhanced PCD- CT in prone position, using breast MRI as the reference standard for breast cancer assessment. Cancer visibility and image quality were rated as very good and non-inferior to MRI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with PCD-CT showing comparable performance to MRI for breast cancer size measurements (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Diagnostic accuracy was good for T-classification (0.814) and axillary lymph node (0.842) and very good for internal mammary lymph node status (0.981). When compared to histopathological findings after surgery, PCD-CT demonstrated good diagnostic accuracy for T-stage (0.781) and axillary lymph node assessment (0.800). However, the accuracy for detecting DCIS as non-mass enhancement was only moderate, both compared to MRI (0.603) and histopathology (0.586). Quantification of iodine uptake in breast carcinomas revealed a significant difference in maximum iodine uptake between NST cancer and NST cancer with associated DCIS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) as well as in mean iodine uptake for positive versus negative estrogen and progesterone receptor status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03 and 0.01) and triple negative versus non-triple negative breast cancers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003eThese findings align with previous studies that demonstrated a higher diagnostic accuracy for breast carcinoma size assessment and T-stage classification using thoracic PCD-CT in prone position compared to digital mammography [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], as well as promising results in assessing breast cancer and regional lymphadenopathy using three-phase thoracic PCD-CT [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We opted for a one-phase protocol, as has been utilized in prior PCD-CT and dual-source CT studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and which demonstrated a high agreement between monoenergetic reconstructions and iodine maps for carcinoma measurements, consistent with prior results from dual energy CT [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn terms of DCIS detection, previous studies reported a better performance of thoracic PCD-CT compared to digital mammography [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and a significant correlation between dedicated breast photon-counting CT and histopathology [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our study, the diagnostic accuracy of PCD-CT for detecting DCIS as non-mass enhancement was only moderate compared to MRI (0.603) and histopathological findings (0.586), which may be attributed to generally lower enhancement and less defined spread of DCIS. Despite of a particularly low sensitivity, the specificity of detecting DCIS was good (0.73), comparable to other studies using multidetector row CT [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We also observed a significantly lower mean iodine uptake in NST tumors that were accompanied by DCIS, which may possibly be attributed to cases of less defined transition of solid carcinoma into low enhancing DCIS. Anyway, PCD-CT with its high spatial resolution [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] may offer greater potential for assessing DCIS when additionally evaluating the detectability of suspicious microcalcifications as has already been explored in a breast phantom study for thoracic PCD-CT [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlso, the capability of differentiating between cancer subtypes such as triple negative and non-triple negative cancer based on iodine uptake as shown in this study aligns with previous research. Evaluations of contrast-enhanced chest CT have indicated a correlation between iodine content and hormone receptor expression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] as well as a capability to distinguish between different cancer types, particularly triple negative and non-triple negative cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, morphological tumor features have been shown to be helpful in differentiating breast cancer subtypes, receptor status, or aggressiveness with dedicated breast CT [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and breast MRI [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. For example, triple negative cancer has been associated with rim enhancement [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] or intratumoral heterogeneity in MRI [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] or correspondingly with higher HU differences within the tumor in CT [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLimitations of our study are the single-center design and the limited number of participants and therefore the limited and unbalanced number of tumor entities. Second, we only included participants with indications for thoracic CT, which, however, is regularly performed for initial staging in breast cancer participants in our health care system. Third, comparison to histopathology after cancer surgery was not possible in all cases due to neoadjuvant or palliative treatment for some participants, anyway, the main aim of the study was a comparison to MRI as the reference standard for imaging in breast cancer assessment. Still, we added the comparison regarding DCIS and lymph node status in cases of initial surgical resection to add more certainty to the status than could be provided by MRI alone. Finally, tumor segmentation and assessment of iodine uptake was only performed by one reader.\u003c/p\u003e\u003cp\u003eIn conclusion, thoracic contrast-enhanced PCD-CT provided excellent cancer visibility and demonstrated a very good performance in comparison to MRI for assessing breast cancer size, T-stage, and lymph nodes. Iodine uptake measurements suggest that PCD-CT may be capable of distinguishing between cancer subtypes, such as NST cancer and NST cancer with associated DCIS and identifying triple negative breast cancer. Additionally, in contrast to other breast-specific imaging modalities, thoracic PCD-CT provides a comprehensive staging of breast cancer, including an assessment of the M-stage, all within a single examination.\u003c/p\u003e\u003cp\u003eFurther studies are required to validate the efficacy and quality of thoracic PCD-CT in local breast cancer assessment and to explore its potential in cancer differentiation and molecular subtyping.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThis prospective study was conducted at a tertiary university medical center from December 2021 to August 2023. All participants with newly diagnosed biopsy-proven breast cancer, who had a current mammography, a medical indication for a thoracoabdominal contrast-enhanced CT in the context of breast cancer staging, and no contraindications for breast MRI were included after written informed consent. Pregnant or breast-feeding women were primarily not included in the study. Exclusion criteria were incomplete or no 3T MRI imaging, imaging after surgery, no invasive cancer, or male patients (compare Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e The present study including the protocols was approved by our institutional review board and in accordance with national guidelines and the Declaration of Helsinki. There are no conflicts of interest.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eThoracic photon-counting detector CT examination and reconstructions\u003c/h2\u003e\u003cp\u003eDual source photon-counting detector CT (NAEOTOM-Alpha; Siemens Healthineers, Germany) examinations were performed in prone position with both breasts hanging freely between pillows comparable to positioning in breast MRI. Our protocol included a single-phase helical acquisition at 120 kVp and a quality reference of 142 mAs after bodyweight\u0026ndash;adapted injection of iopromide (370 mg/mL; Bayer, Germany), followed by a saline chaser with a flow of 3 ml/s and a fixed delay of 85 seconds.\u003c/p\u003e\u003cp\u003eFor evaluation of breasts and axillary lymph node status we added transversal reconstructions with a field of view equivalent to transversal breast MRI scans. These reconstructions were performed at a monoenergetic level of 65 keV, as an iodine map, and a virtual non-contrast reconstruction covering both breasts and the anterior thoracic wall including the axillary region with a fixed field of view of 34 cm, a matrix of 1024x1024 pixels, a slice thickness and increment of 2 mm, kernel Br40, and iterative reconstruction strength 3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBreast MRI examination\u003c/h2\u003e\u003cp\u003eAll participants underwent a multiparametric 3T breast MRI (MAGNETOM Vida; Siemens Healthineers, Germany) within a period of 0 to 17 days (mean 2.29 days, \u0026plusmn; 6.1 SD) following the PCD-CT examination. Breast MRI was performed with an 18-channel breast coil after application of 0.1 mmol/kg contrast agent (Gatoteridol, ProHance, Bracco, Konstanz, Germany). The MRI protocol consisted of a transversal T2 dixon vibe (including in-phase and water only image series), transversal diffusion-weighted imaging, always transversal native followed by four dynamic contrast-enhanced T1-weighted dixon vibe sequences every 90 seconds over 360 seconds altogether with generation of maximum intensity projections as well as subtracted images and additionally ultrafast dynamic breast imaging using TWIST sequences (missing in one case) during contrast agent application. All sequences were used for the evaluation of MRI as reference standard regarding imaging parameters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImage analysis and reference standard\u003c/h2\u003e\u003cp\u003eThe evaluation of pseudonymized PCD-CT examinations was performed by three different experienced radiologists (radiologist 1: 12 years CT imaging and 7 years breast imaging, radiologist 2: 10 years CT imaging and 6 months sole breast imaging, radiologist 3: 6 years CT imaging and 5 months sole breast imaging). Raters were informed about the side of breast malignancy. Otherwise, these raters were blinded to patient data.\u003c/p\u003e\u003cp\u003eThe three raters independently evaluated cancer visibility in all reconstructions, overall confidence in diagnostic assessment, and overall image quality of PCD-CT and MRI imaging on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;excellent; 4\u0026thinsp;=\u0026thinsp;poor). All raters measured the largest tumor diameter in all reconstructions, assessed tumor focality (unifocal versus multifocal/multicentric tumor), skin or pectoralis/thoracic wall infiltration, DCIS suspicious findings (non-mass enhancement). Axillary and internal mammary lymph nodes were assessed by the two most experienced readers and were rated as suspicious when they appeared with a marked asymmetry, a cortical thickening, loss of the fatty hilum, a round and enlarged shape, an irregular margin, a heterogeneous cortex, and/or surrounding edema.\u003c/p\u003e\u003cp\u003eRater 1 also measured the mean and maximum Hounsfield Units (HU) of main tumors in monoenergetic 65 keV reconstructions and iodine maps using a ROI, that was created in the most representative axial slice with the maximum tumor diameter and by manually surrounding the whole solid tumor on that slice for analysis. In case of multifocality or multicentricity the main solid tumor was chosen for this segmentation. Another ROI was positioned in the ascending aorta to assess the relative contrast in cancer versus aortic contrast with mean and maximum HU in monoenergetic 65 keV reconstructions. From mean and maximum HU values in the iodine map, the iodine uptake value in mg/mL was calculated.\u003c/p\u003e\u003cp\u003eThe reference standard (MRI) was evaluated by two experienced radiologists in consensus blinded to ratings from and independent from PCD-CT (radiologist with more than 25 years of CT and breast imaging and another radiologist with 14 years CT imaging and 7 years breast imaging). Regarding presence of DCIS and axillary lymph node assessment, histopathology was considered as reference standard in cases without neoadjuvant treatment and/or prior lymph node biopsy to additionally assess sensitivity and specificity in those cases with definitive histopathology. Information about histologic cancer type and grade, hormone receptor and HER2 status, and Ki-67 index were provided in the histopathological reports. All other clinical data were collected from the medical records.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistics\u003c/h2\u003e\u003cp\u003eFor descriptive statistics continues variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, whereas categorical variables are reported as counts and percentages. Likert-scale ratings of cancer visibility, confidence in diagnostic assessment and image quality are reported as median with interquartile range (IQR), and comparison with reference standard MRI was evaluated by paired non-inferiority tests, which are a variant of a t-test with exchanged hypotheses.\u003c/p\u003e\u003cp\u003eCorrelations between cancer diameter measurements of PCD-CT reconstructions (monoenergetic 65 keV reconstruction versus iodine map) and with reference standard MRI were visualized by scatter plots and quantified by variance explained (adjusted R2) from linear regression. Agreement between PCD-CT and reference standard MRI was assessed by Bland-Altman plots.\u003c/p\u003e\u003cp\u003eDiagnostic accuracy was calculated for PCD-CT with MRI as the reference standard regarding correct T-stage classification, focality, and suspicious axillary and internal mammary lymph nodes. Additionally, in cases of histopathological assessment of lymph nodes, sensitivity and specificity were calculated for PCD-CT with histopathology as the reference standard for lymph node status. The same was performed for non-mass enhancement as a DCIS suspicious finding in case of histopathological proven cancer associated DCIS or cancer surgery.\u003c/p\u003e\u003cp\u003eAgreement between PCD-CT and MRI regarding cancer type (triple negative versus non-triple negative) and cancer necrosis was tested with Fisher\u0026rsquo;s or Chi-square test and Cohen's ω for effect size.\u003c/p\u003e\u003cp\u003eDistribution of average and maximum cancer-to-aorta HU values and iodine uptake values for different histopathological cancer entities was visualized by violin plots and differences were evaluated using the Kruskal-Wallis test by ranks with post-hoc Dunn tests. Comparisons of HU values and iodine uptake for ER status, PR status, HER2 status, or between triple negative versus all other cancer entities together was performed with Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, between triple negative versus all other cancer entities separately with the Kruskal-Wallis test by ranks with post-hoc Dunn tests.\u003c/p\u003e\u003cp\u003eInterrater reliability was calculated using Fleiss\u0026rsquo; kappa for nominal values and intra-class correlation with a two-way-random effects model and a single-rater unit for cancer size measurements. For Fleiss\u0026rsquo; kappa \u003cem\u003eκ\u003c/em\u003e, cutoffs of 0.2, 0.4, 0.6, 0.8, and 1 were considered to denote light, fair, moderate, substantial, and almost perfect agreement, respectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For intra-class correlation a value less than 0.5 indicated poor reliability, values between 0.5 and 0.75 moderate, between 0.75 and 0.9 good, and greater than 0.90 excellent reliability [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eP values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate statistical significance.\u003c/p\u003e\u003cp\u003eWe used R version 4.3.2 for all statistical analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDCIS\u0026nbsp; \u0026nbsp;ductal carcinoma in situ\u003c/p\u003e\n\u003cp\u003eER \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;estrogen receptor\u003c/p\u003e\n\u003cp\u003eHER2\u0026nbsp;\u0026nbsp;human epidermal growth factor receptor 2\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp;interquartile range\u003c/p\u003e\n\u003cp\u003eNST\u0026nbsp; \u0026nbsp; \u0026nbsp;breast cancer of no special type\u003c/p\u003e\n\u003cp\u003ePCD-CT photon-counting detector computed tomography\u003c/p\u003e\n\u003cp\u003ePR \u0026nbsp; \u0026nbsp; \u0026nbsp; progesterone receptor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eData Sharing Statement\u003c/h2\u003e\u003cp\u003eData generated or analyzed during the study are available from the corresponding author by request.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eOnly internal funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll the authors contributed to the various stages of the study and the manuscript preparation.CN and JN developed the concept for this study. FAT, IJB, JN, LJ, MS, OG and SH acquired the patients. FB and MWB provided the devices and the infrastructure. CN, MS and OG prepared the data and MS the image analysis. CN, JN, JW, MMF and MWB performed the image analysis. CN analyzed the data and performed the statistical analysis. CN created the tables and figures. CN wrote the manuscript. All authors read and reviewed the manuscript and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAll authors would like to express their sincere thanks to Dr. Susanne Rospleszcz for the statistical advice in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData generated or analyzed during the study are available from the corresponding author by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBurstein, H. J. et al. 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Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging. \u003cem\u003eEur. Radiol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 822\u0026ndash;833 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Photon-counting detector computed tomography, contrast-enhanced computed tomography, breast cancer, staging, breast cancer subtyping","lastPublishedDoi":"10.21203/rs.3.rs-7863745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7863745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this prospective study, we evaluated the performance of thoracoabdominal photon-counting detector computed tomography (PCD-CT) for breast cancer assessment with MRI as reference standard and iodine uptake as a potential marker for breast cancer subtyping.\u003c/p\u003e\u003cp\u003e75 women (mean age: 55.8 years\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 [SD]) with 79 newly diagnosed breast cancers and indication for staging CT received a prone-positioned contrast-enhanced thoracoabdominal PCD-CT and a breast MRI. Cancer visibility and image quality (median 1/1, IQR 1/0) was rated excellent in PCD-CT on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;excellent, 4\u0026thinsp;=\u0026thinsp;poor). Cancer size in PCD-CT correlated significantly with MRI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Diagnostic accuracy was good for T-stage (accuracy 0.814), focality (0.810), axillary (0.842) and internal mammary lymph nodes (0.981), moderate for ductal carcinoma in situ (0.603). A significant lower maximum iodine uptake was revealed in cancer with ductal carcinoma in situ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), a significant lower mean iodine uptake in triple negative cancers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003eThoracoabdominal PCD-CT demonstrated excellent cancer visibility with convincing results for assessing cancer size, T-stage, and lymph node status. Iodine uptake shows promising associations with triple negative breast cancer.\u003c/p\u003e","manuscriptTitle":"Assessment of Breast Cancer and Feasibility of Subtyping of Breast Cancer using Thoracoabdominal Staging Photon-counting Detector Computed Tomography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:16:49","doi":"10.21203/rs.3.rs-7863745/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T13:45:17+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"217446683038064275230972688384853531845","date":"2026-04-15T11:48:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T14:55:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31000848461985579113756879566237042864","date":"2026-04-13T13:15:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250823278501570826032773636944759065381","date":"2026-04-13T10:39:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322737923074271046234958535763341819554","date":"2026-04-13T10:28:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T18:11:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16231266898813261763353887831874683333","date":"2026-01-23T11:55:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T09:22:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T09:08:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-30T13:11:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T11:36:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-29T11:22:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae3ccd14-4946-4f04-8284-594b5abe60df","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":57372437,"name":"Biological sciences/Cancer"},{"id":57372438,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-19T07:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:16:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7863745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7863745","identity":"rs-7863745","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}