Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography

Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast 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 Research Article Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography Yu-jiao Zhang, Zhu-ming Liang, Xiang-yang Huang, Yan-jing Yu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4975514/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Cone-beam breast computed tomography (CBBCT) can provide detailed information about breast tissue, but whether such information can help predict treatment response is unclear. Purpose To develop a nomogram based on findings from CBBCT as well as conventional clinical variables to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Materials and Methods Medical data were retrospectively analyzed for a consecutive series of women with breast cancer who underwent NAC followed within three months by resection surgery at our hospital between September 2019 and March 2022. Patients were randomized into a development cohort and validation cohort. A nomogram to predict pCR after chemotherapy was formulated based on uni- and multivariate logistic regression of pre-treatment data from the development cohort, and it was tested against data from the validation cohort. The performance of the nomogram was evaluated in terms of the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Of the 215 breast cancer patients in this study, 69 (32.1%) achieved pCR after NAC. Multivariate logistic regression of the development cohort linked such response independently to absence of estrogen receptor (ER) expression, expression of human epidermal growth factor receptor 2 (HER-2), small tumor diameter and non-mass enhancement (NME) on CBBCT. The resulting nomogram predicted response with AUCs of 0.841 (95% CI: 0.78–0.90) in the development cohort (n = 150) and 0.836 (95% CI: 0.74–0.94) in the validation cohort (n = 65), and it was efficient against data from both cohorts based on calibration curves. Decision curve analysis suggested that the nomogram is clinically useful. Conclusion A nomogram incorporating molecular biomarkers and findings from CBBCT may help predict breast cancer patients more likely to respond to NAC. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Globally breast cancer is the most frequent cancer and cause of cancer-related death among women [ 1 , 2 ] . Administering neoadjuvant chemotherapy (NAC) before resection surgery can reduce the volume and stage of breast tumors and improve prognosis [ 3 – 6 ] , making it a popular first-line treatment for locally advanced breast cancer [ 7 – 11 ] . Such treatment is particularly effective when it achieves pathologic complete response (pCR), which is associated with significantly higher recurrence-free survival [ 3 ] . However, fewer than 30% of patients achieve this response [ 3 , 6 , 8 , 12 ] , and other patients may not receive sufficient benefit from NAC to justify the substantial risk of toxic side effects [ 13 ] . These considerations highlight the need for a reliable way to identify patients more likely to show pCR after NAC, which would help individualize treatment planning [ 3 ] . We wondered whether it might be possible to predict pCR using the emerging technique of cone-beam breast computed tomography (CBBCT) [ 14 ] , which can eliminate the influence of tissue overlap to visualize the tumor and its microenvironment in three dimensions [ 15 , 16 ] . While delivering a radiation dose similar to that of mammography, CBBCT can detect breast tumors with sensitivity similar to that of magnetic resonance imaging (MRI) [ 4 , 17 – 19 ] . At the same time, fewer patients have contraindications to CBBCT than to MRI, and tomographic scanning exposes patients to less discomfort [ 15 , 20 , 21 ] . Given that MRI findings have been shown to predict pathologic response to NAC [ 9 , 11 , 13 ] , especially when combined with conventional clinical factors [ 3 , 7 , 10 ] , we wanted to know whether the same might be true of findings from CBBCT. One study has shown that CBBCT findings after NAC were associated with pCR, leading us to wonder whether pre-treatment CBBCT findings could predict pCR [ 4 ] . Thus, we retrospectively analyzed data from breast cancer patients who underwent NAC followed by surgery at our hospital in order to identify clinicodemographic factors and CBBCT findings at baseline that may be associated with pCR after NAC. We found that a nomogram combining the two types of information accurately predicted such response, so it may be a useful tool for personalizing treatment. Methods Patients and neoadjuvant chemotherapy This retrospective study was approved by the Ethics Review Board of Guangxi Medical University Cancer Hospital, which waived the requirement for written informed consent because patients at admission consented to the analysis and publication of their anonymized medical data for research purposes. We screened a consecutive series of patients with breast cancer who underwent NAC followed within three weeks by resection surgery at our hospital between September 2019 and March 2022. We excluded patients if they did not undergo CBBCT before initiating chemotherapy, if their CBBCT images were of inadequate quality to determine breast tissue parameters, if they were biopsied (puncture or excision) before CBBCT, or if they received treatments in addition to NAC and surgery. The patient enrollment flowchart is shown in Fig. 1 . The NAC for all enrolled patients included anthracycline and taxane. Those whose tumors expressed human epidermal growth factor receptor 2 (HER-2) also received trastuzumab. CBBCT All CBBCT in this study was performed on a KBCT-1000 system (Corning, Tianjin, China), which has been approved for clinical use by the Food and Drug Administration in the US and the State Food and Drug Administration in China. Voltage in the X-ray tube was 49 kVp, and current was adjusted between 50 and 160 mA according to the volume and density of the breast. Patients lay prone on the examination bed, and the breast was allowed to drop naturally into the scanning area. The ipsilateral arm lay naturally at the body's side, while the contralateral arm was raised up. The healthy breast was scanned first, followed by the affected breast. Each breast was scanned for 8 sec. Next, CBBCT was performed by injecting patients intravenously with the non-ionic contrast agent iodohexyl alcohol (350 mg I/mL) using a double-chamber syringe at a rate of 2 mL/sec up to a cumulative dose of 1.5-2.0 mL/kg. The healthy and affected breasts were scanned at 60 and 110 sec after the end of contrast agent injection. All CBBCT images were retrospectively reviewed independently by two radiologists, both of whom had more than 10 years of experience and were blinded to other data about the patients. Any discrepancies in the assessments by the two radiologists were resolved through discussion. The radiologists assessed the images for abnormal enhancement, which they classified as "mass enhancement" or "non-mass enhancement" (NME); and for the presence of dense breast tissue, which was defined as tissue showing compositions C or D according to published guidelines [ 22 ] . They also measured tumor diameter by selecting the longest of the diameters determined from horizontal, sagittal and coronal images. For patients with multiple lesions, the tumor diameter was defined as the diameter of the largest tumor. The rate of enhancement was measured during phase I and phase II acccording to the formula: Rate = (attenuation before enhancement - attenuation after enhancement) / attenuation before enhancement × 100%. Some descriptive features of breast tumors were adapted from features routinely assessed during mammography or MRI as described in the BI-RADS Atlas [ 22 ] and other literature. Several other disease features were extracted from CBBCT scans as described [ 15 , 23 ] , including microcalcification, irregular margins, lobulation, spiculation, and Increased vascularity. Pathologic analysis before NAC Breast tissue of all patients was biopsied after CBBCT and before NAC. Biopsies were immunostained against estrogen receptor (ER) and progesterone receptor (PR). Expression of HER-2 was assessed using immunohistochemistry and/or fluorescence in situ hybridization. Positive staining of > 1% of all examined cells was defined as the presence of expression. Assessment of pathologic response after NAC Breast tissue was excised after the last NAC cycle and assessed for pathologic response using histopathology and the Miller-Payne system [ 24 ] . Complete response was defined as grade 5, corresponding to the absence of histological evidence of malignancy at the site of the primary tumor, or the presence of only carcinoma in situ . Development of a nomogram to predict pCR Patients were randomly allocated in the ratio 7:3 into a development cohort and a validation cohort. Univariate logistic regression was performed on data from the development cohort in order to identify pre-treatment factors significantly associated with pCR to NAC. Factors that were associated with P < 0.05 were included in multivariable logical stepwise regression to identify independent predictors of pCR. The resulting predictors were assembled into a nomogram, whose predictive performance against data from the validation cohort was assessed in terms of area under the receiver operating characteristic curve (AUC), calibration plots, and decision curves. Other statistical analyses Continuous data were reported as mean ± SD if normally distributed or as median (interquartile range) if skewed. Intergroup differences were assessed for significance using the Mann–Whitney U test. Categorical data were reported as n (%), and intergroup differences were assessed using the chi-squared or Fisher’s exact test. Results We included 215 breast cancer patients in the study, and their randomization led to development and validation cohorts that did not differ significantly from each other in any of the baseline characteristics that we examined (Table 1 ), or in the rate of pCR (Table 2 ). Univariate analysis identified the following variables as associated with such response (Table 3 ): postmenopausal status; expression status of ER, PR and HER-2; Ki-67 index; and the three CBBCT parameters of tumor diameter, whether enhancement was mass or NME, and whether breast tissue was dense. Of these variables, the ones that emerged from multivariable analysis as independent predictors of pCR were ER negative, HER-2 positive, small tumor diameter, and NME. Representative examples of breast tumors showing pCR or no-pCR are given in Figs. 2 and 3 , respectively. Using these four independent predictors, we constructed a nomogram to predict pCR after NAC (Fig. 4 ). The nomogram showed an AUC of 0.841 (95% CI: 0.78–0.90) in the development cohort and 0.836 (95% CI: 0.74–0.94) in the validation cohort (Fig. 5 ), indicating good predictive ability. In both cohorts, calibration curves indicated that the nomogram was efficient (Fig. 6 ). Decision curve analysis of both cohorts showed that if the threshold probability was > 5%, the nomogram was superior to assuming that all patients would show pCR or that none would (Fig. 7 ). Discussion Here we provide evidence that the combination of two routinely assessed characteristics of breast cancer and two parameters determined from CBBCT can help predict which patients are more likely to show pCR after NAC. The nomogram included four pre-treatment factors as independent predictors: ER negative, HER-2 positive, small tumor diameter, and NME. In our cohort, the findings of ER negative and HER-2 positive independently predicted pCR. Previous studies have linked each finding to greater likelihood of pCR [25-27] , perhaps because tumors with these characteristics proliferate quite fast, making them more sensitive to NAC [27, 28] . The fact that patients with HER-2 positive breast cancer received trastuzumab, a specific targeted therapy for HER2-positive tumors, may also have contributed to the observed association between HER-2 expression and pCR [29-31] . In our cohort, NME based on CBBCT was associated with higher probability of pCR. The majority of previous research on NME has focused on the differentiation between benign and malignant lesions [32-35] , and we are unaware of literature specifically addressing the association between NME and the probability of pCR after NAC. According to our study, the probability of achieving pCR after NAC in patients with NME on CBBCT is 3.2-fold higher than that in patients whose lesions show mass enhancement. This finding extends the clinical usefulness of NME. NME may be associated with tumors that are more aggressive and proliferate more rapidly, and are therefore more vulnerable to chemotherapy. Smaller tumors before NAC were associated with greater likelihood of pCR in our cohort. Reduction of tumor burden is the important factor to achieve pCR after NAC [36, 37] . Previous studies have found that chemotherapy drugs can reduce tumor cell structure to varying degrees. The extent of tumor cell loss after NAC is important for assessing the response to NAC [27] . The smaller the diameter of the tumor before NAC, the greater the proportion of cell structure destroyed by the chemotherapy drug, and the easier it is to achieve pCR. Although CBBCT, unlike MRI, can display calcification stereoscopically [4] , the absence or presence of microcalcification in breast tissue was not associated with likelihood of pCR in our cohort. While some work has suggested that changes in calcifications after NAC correlate with tumor response to NAC [38, 39] , other studies have observed no association between microcalcification and pCR [40, 41] . Future research should explore the prognostic significance, if any, of breast microcalcifications in patients undergoing NAC. Our findings should be interpreted with caution given that our sample came from a single center and was analyzed retrospectively, increasing risk of selection bias. Our results should be verified and extended in larger, preferably prospective studies from multiple centers. Such work should take into account axillary status as a potential predictor of response to NAC, since axillary status after NAC is beneficial to the selection of surgical methods after NAC. [42] . Consensus guidelines on interpreting images from CBBCT are needed in order to ensure that consistent practices lead to robust evidence for or against using this imaging technique to diagnose breast cancer and predict prognosis. Despite these limitations, our results suggest that the combination of conventional clinical characteristics and two parameters from CBBCT, NME and tumor diameter, may improve the prediction of which breast cancer patients will show pCR after NAC. Both of those CBBCT parameters can also be assessed using MRI and breast mammography, implying that our nomogram may be compatible with other imaging techniques. Declarations Funding This research was funded by Natural Science Foundation of Guangxi Province ( Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YJZ, YNJ, NBL, XYH, ZML, YJY, YNM. 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Eur J Radiol. 2019;118:114–21. Kuehn T, Bauerfeind I, Fehm T, et al. Sentinel-lymph-node biopsy in patients with breast cancer before and after neoadjuvant chemotherapy (SENTINA): a prospective, multicentre cohort study. Lancet Oncol. 2013;14(7):609–18. Tables Table 1 . Clinicodemographic characteristics of patients in the study Characteristic All patients (N = 215) Development cohort (n = 150) Validation cohort (n = 65) P Menstrual status 0.157 Postmenopausal 78 59 19 Premenopausal 137 91 46 Expression of ER 0.648 Negative 81 58 23 Positive 134 92 42 Expression of PR 0.648 Negative 81 58 23 Positive 134 92 42 Expression of HER-2 0.648 Negative 114 78 36 Positive 101 72 29 Ki-67 index 46.9±22.9 47.3±22.7 45.9±23.6 0.580 pCR Yes No 69 146 51 99 18 47 0.363 Values are mean ± SD or n, unless otherwise noted. ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; pCR, pathologic complete response. Table 2. Pre-treatment CBBCT findings for patients in the study. Characteristic All patients (N = 215) Development cohort (n = 150) Validation cohort (n = 65) P Tumor diameter, cm 3.9±1.7 3.9±1.7 3.8±1.7 0.314 Type of enhancement 0.458 Mass 172 104 54 NME 43 32 11 Multifocal disease 0.711 Absent 63 45 18 Present 151 104 47 Unknown 1 1 0 Dense tissue 0.620 Absent 51 37 14 Present 164 113 51 Microcalcification 0.122 Absent 73 46 27 Present 142 104 38 Irregular margins 0.426 Absent 54 40 14 Present 161 110 51 Lobulation 0.118 Absent 40 32 8 Present 175 118 57 Spiculation 0.506 Absent 100 72 28 Present 115 78 37 Increased vascularity 0.590 Absent 48 35 13 Present 167 115 52 Phase I enhancement rate, % 2.5±5.3 2.1±3.5 3.3±8.0 0.108 Phase II enhancement rate, % 2.5±5.4 2.1±3.0 3.5±8.7 0.095 Values are mean ± SD or n, unless otherwise noted. CBBCT, cone-beam breast computed tomography; NME, non-mass enhancement. Table 3. Uni- and multivariate analysis of the development cohort to identify pre-treatment factors associated with pCR to neoadjuvant chemotherapy Factor Univariate Multivariate OR (95% CI) P OR (95% CI) P Postmenopausal status 2.20 (1.06-4.58) 0.034 1.76 (0.65-4.80) 0.271 ER negative 7.23 (3.40-15.35） ＜ 0.001 6.80 (2.02-22.92) 0.002 PR negative 2.80 (1.39-5.63） 0.004 1.13 (0.37-3.50) 0.833 HER-2 positive 4.8 (2.30-10.13) ＜ 0.001 3.90 (1.57-9.73) 0.003 Ki-67 index 1.02 (1.00-1.04) 0.015 1.01 (0.98-1.03) 0.641 Tumor diameter 0.78 (0.62-0.98) 0.028 0.61 (0.44-0.86) 0.004 NME 2.80 (1.26-6.24) 0.012 3.20 (1.01-10.21) 0.049 Multifocal disease present 0.80 (0.39-0.1.66） 0.548 Dense tissue type present 4.40 (1.60-12.12) 0.004 2.89 (0.79-10.54) 0.108 Microcalcification present 0.72 (0.35-1.49) 0.379 Irregular margins present 0.52 (0.25-1.10) 0.089 Lobulation present 0.50 (0.22-1.10) 0.086 Spiculation present 0.52 (0.26-1.02) 0.058 Increased vascularity present 0.71 (0.33-1.55) 0.393 Phase I enhancement rate 1.04 (0.95-1.15) 0.397 Phase II enhancement rate 1.04 (0.94-1.16) 0.445 CI, confidence interval; ER, estrogen receptor; OR, odds ratio; PR, progesterone receptor; HER-2, human epidermal growth factor receptor 2; NME, non-mass enhancement; pCR, pathologic complete response. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2024 Editor assigned by journal 28 Aug, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 26 Aug, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4975514","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346246038,"identity":"4fde91d7-6c87-48e9-b1e0-bad0dbe92ce0","order_by":0,"name":"Yu-jiao Zhang","email":"","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-jiao","middleName":"","lastName":"Zhang","suffix":""},{"id":346246039,"identity":"56cb75b2-25ee-440f-9226-8b5611fac01e","order_by":1,"name":"Zhu-ming Liang","email":"","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhu-ming","middleName":"","lastName":"Liang","suffix":""},{"id":346246040,"identity":"6020016f-1f8e-4f5f-b59d-d6cb03340c5d","order_by":2,"name":"Xiang-yang Huang","email":"","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang-yang","middleName":"","lastName":"Huang","suffix":""},{"id":346246041,"identity":"df30e66e-9fec-4520-a200-3757a484f326","order_by":3,"name":"Yan-jing Yu","email":"","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan-jing","middleName":"","lastName":"Yu","suffix":""},{"id":346246042,"identity":"7c31aa89-cf58-497b-94b6-65dba855e0b6","order_by":4,"name":"Ya-nan Mo","email":"","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ya-nan","middleName":"","lastName":"Mo","suffix":""},{"id":346246043,"identity":"b95a97cb-e924-4d2b-8ae8-eade2fdb67f7","order_by":5,"name":"Ning-bin Luo","email":"","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ning-bin","middleName":"","lastName":"Luo","suffix":""},{"id":346246044,"identity":"5e536167-837e-425a-9685-838215fb4760","order_by":6,"name":"Yi-nan Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACxgYG9h8JFTYJYF5CAXFaGCQenElLYGADaTEg0ibJhy2HIVoYiNHCPCP3gEFiw/k8fvnuxA8PDBjk+cUOEHDYjLyEhMQdt4sl23g3SwAdZjhzdgIhLTkGBxLP3E7ccIx3A0hLgsFtwloMGxLbzoG0bP5BrBZjhsS2AyAt24i0peeNGUPCmeTEmW252ywSDCQI+8WwPceM8UeFXWI/89nNN39U2MjzSxPSMgFVgQR+5SAgz3+AsKJRMApGwSgY4QAAAYtIB7bqeHUAAAAASUVORK5CYII=","orcid":"","institution":"Tumor Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yi-nan","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2024-08-26 06:00:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4975514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4975514/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66855273,"identity":"72463bc8-5208-4a5b-9778-38662458d306","added_by":"auto","created_at":"2024-10-17 07:39:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35165,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/c9a1b3de448801b1d1db98c9.png"},{"id":66855931,"identity":"e47427d1-14e8-4641-a2f3-2f58bb1a78db","added_by":"auto","created_at":"2024-10-17 07:47:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177590,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative results from a patient in our cohort who showed pCR. The 54-year-old woman had an invasive breast tumor that was ER negative and HER-2 positive. (\u003cstrong\u003ea-c\u003c/strong\u003e) Horizontal, sagittal, and coronal images of the breast from CBBCT before neoadjuvant chemotherapy, revealing a 4-cm tumor with NME (arrow). (\u003cstrong\u003ed-e\u003c/strong\u003e) Horizontal and sagittal images from CBBCT after chemotherapy. (\u003cstrong\u003ef\u003c/strong\u003e) The patient's nomogram, where the points for each factor were summed to obtain an overall score reflecting the probability of pCR (85%). ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; NME, non-mass enhancement; pCR, pathologic complete response.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/33ca26d11dddc65cf9ae3842.png"},{"id":66855272,"identity":"472dc724-bb2e-4b7e-908e-cbaec9ee7130","added_by":"auto","created_at":"2024-10-17 07:39:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":330570,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative results from a ptient in our cohort who did not show pCR. The 53-year-old woman had invasive breast cancer that was ER positive and HER-2 positive. (\u003cstrong\u003ea-c\u003c/strong\u003e) Horizontal, sagittal, and coronal images of the breast from CBBCT before neoadjuvant chemotherapy, revealing a 4.3-cm tumor with mass enhancement (arrow). (\u003cstrong\u003ed-e\u003c/strong\u003e) Horizontal and sagittal images from CBBCT after chemotherapy. (\u003cstrong\u003ef\u003c/strong\u003e) The patient's nomogram, which indicated a probability of achieving pCR of 20%. ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; NME, non-mass enhancement; pCR, pathologic complete response.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/80aebed7b5e7f1e13f599729.png"},{"id":66855267,"identity":"a9f6d93e-0a05-4d6e-8462-09d6f65d07f0","added_by":"auto","created_at":"2024-10-17 07:39:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17467,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting pathologic complete response in patients with breast cancer after neoadjuvant chemotherapy, based on pre-treatment characteristics. The scores for each characteristic are summed to obtain a total score, which corresponds to a probability of pathologic complete response. ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; NME, non-mass enhancement; pCR, pathologic complete response.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/d1850a5d3e0545b94cc600e8.png"},{"id":66857574,"identity":"63c8e721-268b-4a32-9858-8ba1afd01d73","added_by":"auto","created_at":"2024-10-17 07:55:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39224,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves to assess the nomogram's ability to predict pathologic complete response to neoadjuvant chemotherapy in the (\u003cstrong\u003ea\u003c/strong\u003e) development cohort and (\u003cstrong\u003eb\u003c/strong\u003e) validation cohort. Areas under the curves (AUC) are shown.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/059e82b00eaa43eeaa1e0a52.png"},{"id":66855268,"identity":"b72c5420-16ad-4800-bdde-7322872b776c","added_by":"auto","created_at":"2024-10-17 07:39:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27327,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves to assess nomogram efficiency in predicting pathologic complete response to neoadjuvant chemotherapy in the (\u003cstrong\u003ea\u003c/strong\u003e) development cohort and (\u003cstrong\u003eb\u003c/strong\u003e) validation cohort.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/af51deeadc5bf37e0181f3d2.png"},{"id":66855270,"identity":"edfe6bb2-0472-4e9f-b34a-4ebb218115fb","added_by":"auto","created_at":"2024-10-17 07:39:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":26186,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis to assess nomogram efficiency in predicting pathologic complete response to neoadjuvant chemotherapy in the \u003cstrong\u003e(a)\u003c/strong\u003e development cohort and (\u003cstrong\u003eb\u003c/strong\u003e) internal validation cohort. The y-axis indicates the net benefit, and the x-axis indicates threshold probability. The red line represents the prediction model. The net benefit of the assumption that all patients achieve pathologic complete response (gray line) or the assumption that none do (black line) is shown as a reference. If the decision curve shows a greater net benefit than both assumptions, the predictive model is considered to provide clinical benefit.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/c4fd78f15a6b150d050373e6.png"},{"id":66858069,"identity":"f3867c3b-4af1-4684-8c81-997c7e66d116","added_by":"auto","created_at":"2024-10-17 08:03:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4975514/v1/03d8f674-c1f2-42f2-8fb8-cfca7e9d1844.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally breast cancer is the most frequent cancer and cause of cancer-related death among women\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Administering neoadjuvant chemotherapy (NAC) before resection surgery can reduce the volume and stage of breast tumors and improve prognosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, making it a popular first-line treatment for locally advanced breast cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Such treatment is particularly effective when it achieves pathologic complete response (pCR), which is associated with significantly higher recurrence-free survival\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, fewer than 30% of patients achieve this response\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and other patients may not receive sufficient benefit from NAC to justify the substantial risk of toxic side effects\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These considerations highlight the need for a reliable way to identify patients more likely to show pCR after NAC, which would help individualize treatment planning\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe wondered whether it might be possible to predict pCR using the emerging technique of cone-beam breast computed tomography (CBBCT)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, which can eliminate the influence of tissue overlap to visualize the tumor and its microenvironment in three dimensions\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. While delivering a radiation dose similar to that of mammography, CBBCT can detect breast tumors with sensitivity similar to that of magnetic resonance imaging (MRI)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. At the same time, fewer patients have contraindications to CBBCT than to MRI, and tomographic scanning exposes patients to less discomfort\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Given that MRI findings have been shown to predict pathologic response to NAC\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, especially when combined with conventional clinical factors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, we wanted to know whether the same might be true of findings from CBBCT. One study has shown that CBBCT findings after NAC were associated with pCR, leading us to wonder whether pre-treatment CBBCT findings could predict pCR \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThus, we retrospectively analyzed data from breast cancer patients who underwent NAC followed by surgery at our hospital in order to identify clinicodemographic factors and CBBCT findings at baseline that may be associated with pCR after NAC. We found that a nomogram combining the two types of information accurately predicted such response, so it may be a useful tool for personalizing treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and neoadjuvant chemotherapy\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the Ethics Review Board of Guangxi Medical University Cancer Hospital, which waived the requirement for written informed consent because patients at admission consented to the analysis and publication of their anonymized medical data for research purposes. We screened a consecutive series of patients with breast cancer who underwent NAC followed within three weeks by resection surgery at our hospital between September 2019 and March 2022. We excluded patients if they did not undergo CBBCT before initiating chemotherapy, if their CBBCT images were of inadequate quality to determine breast tissue parameters, if they were biopsied (puncture or excision) before CBBCT, or if they received treatments in addition to NAC and surgery. The patient enrollment flowchart is shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe NAC for all enrolled patients included anthracycline and taxane. Those whose tumors expressed human epidermal growth factor receptor 2 (HER-2) also received trastuzumab.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCBBCT\u003c/h2\u003e \u003cp\u003e All CBBCT in this study was performed on a KBCT-1000 system (Corning, Tianjin, China), which has been approved for clinical use by the Food and Drug Administration in the US and the State Food and Drug Administration in China. Voltage in the X-ray tube was 49 kVp, and current was adjusted between 50 and 160 mA according to the volume and density of the breast. Patients lay prone on the examination bed, and the breast was allowed to drop naturally into the scanning area. The ipsilateral arm lay naturally at the body's side, while the contralateral arm was raised up. The healthy breast was scanned first, followed by the affected breast. Each breast was scanned for 8 sec. Next, CBBCT was performed by injecting patients intravenously with the non-ionic contrast agent iodohexyl alcohol (350 mg I/mL) using a double-chamber syringe at a rate of 2 mL/sec up to a cumulative dose of 1.5-2.0 mL/kg. The healthy and affected breasts were scanned at 60 and 110 sec after the end of contrast agent injection.\u003c/p\u003e \u003cp\u003eAll CBBCT images were retrospectively reviewed independently by two radiologists, both of whom had more than 10 years of experience and were blinded to other data about the patients. Any discrepancies in the assessments by the two radiologists were resolved through discussion. The radiologists assessed the images for abnormal enhancement, which they classified as \"mass enhancement\" or \"non-mass enhancement\" (NME); and for the presence of dense breast tissue, which was defined as tissue showing compositions C or D according to published guidelines\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. They also measured tumor diameter by selecting the longest of the diameters determined from horizontal, sagittal and coronal images. For patients with multiple lesions, the tumor diameter was defined as the diameter of the largest tumor. The rate of enhancement was measured during phase I and phase II acccording to the formula:\u003c/p\u003e \u003cp\u003eRate = (attenuation before enhancement - attenuation after enhancement) / attenuation before enhancement \u0026times; 100%.\u003c/p\u003e \u003cp\u003eSome descriptive features of breast tumors were adapted from features routinely assessed during mammography or MRI as described in the BI-RADS Atlas\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e and other literature. Several other disease features were extracted from CBBCT scans as described\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, including microcalcification, irregular margins, lobulation, spiculation, and Increased vascularity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePathologic analysis before NAC\u003c/h2\u003e \u003cp\u003eBreast tissue of all patients was biopsied after CBBCT and before NAC. Biopsies were immunostained against estrogen receptor (ER) and progesterone receptor (PR). Expression of HER-2 was assessed using immunohistochemistry and/or fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization. Positive staining of \u0026gt;\u0026thinsp;1% of all examined cells was defined as the presence of expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of pathologic response after NAC\u003c/h2\u003e \u003cp\u003eBreast tissue was excised after the last NAC cycle and assessed for pathologic response using histopathology and the Miller-Payne system\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Complete response was defined as grade 5, corresponding to the absence of histological evidence of malignancy at the site of the primary tumor, or the presence of only carcinoma \u003cem\u003ein situ\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a nomogram to predict pCR\u003c/h2\u003e \u003cp\u003ePatients were randomly allocated in the ratio 7:3 into a development cohort and a validation cohort. Univariate logistic regression was performed on data from the development cohort in order to identify pre-treatment factors significantly associated with pCR to NAC. Factors that were associated with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in multivariable logical stepwise regression to identify independent predictors of pCR. The resulting predictors were assembled into a nomogram, whose predictive performance against data from the validation cohort was assessed in terms of area under the receiver operating characteristic curve (AUC), calibration plots, and decision curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOther statistical analyses\u003c/h2\u003e \u003cp\u003eContinuous data were reported as mean \u0026plusmn; SD if normally distributed or as median (interquartile range) if skewed. Intergroup differences were assessed for significance using the Mann\u0026ndash;Whitney U test. Categorical data were reported as n (%), and intergroup differences were assessed using the chi-squared or Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 215 breast cancer patients in the study, and their randomization led to development and validation cohorts that did not differ significantly from each other in any of the baseline characteristics that we examined (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), or in the rate of pCR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnivariate analysis identified the following variables as associated with such response (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): postmenopausal status; expression status of ER, PR and HER-2; Ki-67 index; and the three CBBCT parameters of tumor diameter, whether enhancement was mass or NME, and whether breast tissue was dense. Of these variables, the ones that emerged from multivariable analysis as independent predictors of pCR were ER negative, HER-2 positive, small tumor diameter, and NME. Representative examples of breast tumors showing pCR or no-pCR are given in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003eUsing these four independent predictors, we constructed a nomogram to predict pCR after NAC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nomogram showed an AUC of 0.841 (95% CI: 0.78\u0026ndash;0.90) in the development cohort and 0.836 (95% CI: 0.74\u0026ndash;0.94) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating good predictive ability. In both cohorts, calibration curves indicated that the nomogram was efficient (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Decision curve analysis of both cohorts showed that if the threshold probability was \u0026gt;\u0026thinsp;5%, the nomogram was superior to assuming that all patients would show pCR or that none would (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere we provide evidence that the combination of two routinely assessed characteristics of breast cancer and two parameters determined from CBBCT can help predict which patients are more likely to show pCR after NAC. The nomogram included four pre-treatment factors as independent predictors: ER negative, HER-2 positive, small tumor diameter, and NME.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our cohort, the findings of ER negative and HER-2 positive independently predicted pCR. Previous studies have linked each finding to greater likelihood of pCR\u003csup\u003e[25-27]\u003c/sup\u003e, perhaps because tumors with these characteristics proliferate quite fast, making them more sensitive to NAC\u003csup\u003e[27, 28]\u003c/sup\u003e. The fact that patients with HER-2 positive breast cancer received trastuzumab, a specific targeted therapy for HER2-positive tumors, may also have contributed to the observed association between HER-2 expression and pCR\u003csup\u003e[29-31]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn our cohort, NME based on CBBCT was associated with higher probability of pCR. The majority of previous research on NME has focused on the differentiation between benign and malignant lesions\u003csup\u003e[32-35]\u003c/sup\u003e, and we are unaware of literature specifically addressing the association between NME and the probability of pCR after NAC. According to our study, the probability of achieving pCR after NAC in patients with NME on CBBCT is 3.2-fold higher than that in patients whose lesions show mass enhancement. This finding extends the clinical usefulness of NME. NME may be associated with tumors that are more aggressive and proliferate more rapidly, and are therefore more vulnerable to chemotherapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmaller tumors before NAC were associated with greater likelihood of pCR in our cohort. Reduction of tumor burden is the important factor to achieve pCR after NAC\u003csup\u003e[36, 37]\u003c/sup\u003e. Previous studies have found that chemotherapy drugs can reduce tumor cell structure to varying degrees. The extent of tumor cell loss after NAC is important for assessing the response to NAC\u003csup\u003e[27]\u003c/sup\u003e. The smaller the diameter of the tumor before NAC, the greater the proportion of cell structure destroyed by the chemotherapy drug, and the easier it is to achieve pCR. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough CBBCT, unlike MRI, can display calcification stereoscopically\u003csup\u003e[4]\u003c/sup\u003e, the absence or presence of microcalcification in breast tissue was not associated with likelihood of pCR in our cohort. While some work has suggested that changes in calcifications after NAC correlate with tumor response to NAC\u003csup\u003e[38, 39]\u003c/sup\u003e, other studies have observed no association between microcalcification and pCR\u003csup\u003e[40, 41]\u003c/sup\u003e. Future research should explore the prognostic significance, if any, of breast microcalcifications in patients undergoing NAC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings should be interpreted with caution given that our sample came from a single center and was analyzed retrospectively, increasing risk of selection bias. Our results should be verified and extended in larger, preferably prospective studies from multiple centers. Such work should take into account axillary status as a potential predictor of response to NAC, since axillary status after NAC is beneficial to the selection of surgical methods after NAC.\u003csup\u003e[42]\u003c/sup\u003e. Consensus guidelines on interpreting images from CBBCT are needed in order to ensure that consistent practices lead to robust evidence for or against using this imaging technique to diagnose breast cancer and predict prognosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these limitations, our results suggest that the combination of conventional clinical characteristics and two parameters from CBBCT, NME and tumor diameter, may improve the prediction of which breast cancer patients will show pCR after NAC. Both of those CBBCT parameters can also be assessed using MRI and breast mammography, implying that our nomogram may be compatible with other imaging techniques.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Natural Science Foundation of Guangxi Province (\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YJZ, YNJ, NBL, XYH, ZML, YJY, YNM. The first draft of the manuscript was written by YJZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaysal H, Serdaroglu AY, Ozemir IA, et al. Comparison of Magnetic Resonance Imaging With Positron Emission Tomography/Computed Tomography in the Evaluation of Response to Neoadjuvant Therapy of Breast Cancer. J Surg Res. 2022;278:223\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthias H, Cl\u0026eacute;mence B, Electra S, et al. Diagnostic precision of breast MRI in prediction of pathological complete response: Is it influenced by the presence of metallic markers. Eur J Radiol. 2022;154:110453.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao R, Lu H, Li YB, Shao ZZ, Ma WJ, Liu PF. Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI. Acad Radiol. 2022;29(Suppl 1):S155\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Li S, Zhou C et al. Assessment of Cone-Beam Breast Computed Tomography for Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer: A Prospective Study. J Oncol. 2022. 2022: 9321763.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarinovich ML, Sardanelli F, Ciatto S, et al. Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI. Breast. 2012;21(5):669\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomeo V, Accardo G, Perillo T et al. 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Radiol Med. 2024;129(5):737\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWienbeck S, Andrijevska V, K\u0026uuml;ck F, et al. Comparison between cone-beam breast-CT and full-field digital mammography for microcalcification detection depending on breast density. Med (Baltim). 2023;102(22):e33900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa WM, Li J, Chen SG, et al. Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer. Br J Radiol. 2022;95(1132):20210466.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpak DA, Plaxco JS, Santiago L, Dryden MJ, Dogan BE. BI-RADS\u0026reg; fifth edition: A summary of changes. Diagn Interv Imaging. 2017;98(3):179\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSardanelli F, Iozzelli A, Fausto A, Carriero A, Kirchin MA. Gadobenate dimeglumine-enhanced MR imaging breast vascular maps: association between invasive cancer and ipsilateral increased vascularity. Radiology. 2005;235(3):791\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgston KN, Miller ID, Payne S, et al. A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival. Breast. 2003;12(5):320\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvenzano E. Neoadjuvant Chemotherapy for Breast Cancer: Moving Beyond Pathological Complete Response in the Molecular Age. Acta Med Acad. 2021;50(1):88\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim R, Chang JM, Lee HB, et al. Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer. Radiology. 2019;293(1):49\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi WJ, Kim WK, Shin HJ, Cha JH, Chae EY, Kim HH. Evaluation of the Tumor Response After Neoadjuvant Chemotherapy in Breast Cancer Patients: Correlation Between Dynamic Contrast-enhanced Magnetic Resonance Imaging and Pathologic Tumor Cellularity. Clin Breast Cancer. 2018;18(1):e115\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin HJ, Baek HM, Ahn JH, et al. Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and MRS. NMR Biomed. 2012;25(12):1349\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JH, Feig B, Agrawal G, et al. MRI evaluation of pathologically complete response and residual tumors in breast cancer after neoadjuvant chemotherapy. Cancer. 2008;112(1):17\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCameron D, Piccart-Gebhart MJ, Gelber RD, et al. 11 years' follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive early breast cancer: final analysis of the HERceptin Adjuvant (HERA) trial. Lancet. 2017;389(10075):1195\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Minckwitz G, Huang CS, Mano MS, et al. Trastuzumab Emtansine for Residual Invasive HER2-Positive Breast Cancer. N Engl J Med. 2019;380(7):617\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChadashvili T, Ghosh E, Fein-Zachary V, et al. Nonmass enhancement on breast MRI: review of patterns with radiologic-pathologic correlation and discussion of management. AJR Am J Roentgenol. 2015;204(1):219\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu G, Li Y, Chen SL, Chen Q. Non-mass enhancement breast lesions: MRI findings and associations with malignancy. Ann Transl Med. 2022;10(6):357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTozaki M, Fukuda K. High-spatial-resolution MRI of non-masslike breast lesions: interpretation model based on BI-RADS MRI descriptors. AJR Am J Roentgenol. 2006;187(2):330\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenwood HI, Wilmes LJ, Kelil T, Joe BN. Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges. J Magn Reson Imaging. 2020;52(3):697\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNahleh Z, Sivasubramaniam D, Dhaliwal S, Sundarajan V, Komrokji R. Residual cancer burden in locally advanced breast cancer: a superior tool. Curr Oncol. 2008;15(6):271\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSymmans WF, Peintinger F, Hatzis C, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25(28):4414\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYim H, Ha T, Kang DK, Park SY, Jung Y, Kim TH. Change in microcalcifications on mammography after neoadjuvant chemotherapy in breast cancer patients: correlation with tumor response grade and comparison with lesion extent. Acta Radiol. 2019;60(2):131\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazari F, Sharma N, Dodwell D, Horgan K. Human Epidermal Growth Factor 2-positive Breast Cancer with Mammographic Microcalcification: Relationship to Pathologic Complete Response after Neoadjuvant Chemotherapy. Radiology. 2018;288(2):366\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JJ, Chen C, Gu Y, et al. The role of mammographic calcification in the neoadjuvant therapy of breast cancer imaging evaluation. PLoS ONE. 2014;9(2):e88853.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi WJ, Kim HH, Cha JH, Shin HJ, Chae EY, Yoon GY. Complete response on MR imaging after neoadjuvant chemotherapy in breast cancer patients: Factors of radiologic-pathologic discordance. Eur J Radiol. 2019;118:114\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuehn T, Bauerfeind I, Fehm T, et al. Sentinel-lymph-node biopsy in patients with breast cancer before and after neoadjuvant chemotherapy (SENTINA): a prospective, multicentre cohort study. Lancet Oncol. 2013;14(7):609\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Clinicodemographic characteristics of patients in the study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 215)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevelopment cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 150)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003eMenstrual status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Postmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Premenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003eExpression of ER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003eExpression of PR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40.681%;\"\u003e\n \u003cp\u003eExpression of HER-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.448%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003eKi-67 index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e46.9\u0026plusmn;22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e47.3\u0026plusmn;22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e45.9\u0026plusmn;23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6559%;\"\u003e\n \u003cp\u003epCR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0251%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.448%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6559%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2151%;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are mean \u0026plusmn; SD or n, unless otherwise noted. ER, estrogen receptor; HER-2, human epidermal growth factor receptor 2; pCR, pathologic complete response.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Pre-treatment CBBCT findings for patients in the study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 215)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevelopment cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 150)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eTumor diameter, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e3.9\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e3.9\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e3.8\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eType of enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; NME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eMultifocal disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eDense tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eMicrocalcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eIrregular margins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eLobulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eSpiculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003eIncreased vascularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003ePhase I enhancement rate, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e2.5\u0026plusmn;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e2.1\u0026plusmn;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e3.3\u0026plusmn;8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8696%;\"\u003e\n \u003cp\u003ePhase II enhancement rate, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e2.5\u0026plusmn;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.471%;\"\u003e\n \u003cp\u003e2.1\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3986%;\"\u003e\n \u003cp\u003e3.5\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8623%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are mean \u0026plusmn; SD or n, unless otherwise noted. CBBCT, cone-beam breast computed tomography; NME, non-mass enhancement.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Uni- and multivariate analysis of the development cohort to identify pre-treatment factors associated with pCR to neoadjuvant chemotherapy\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003ePostmenopausal status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.20 (1.06-4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.76 (0.65-4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eER negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.23 (3.40-15.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6.80 (2.02-22.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003ePR negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.80 (1.39-5.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.13 (0.37-3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eHER-2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.8 (2.30-10.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.90 (1.57-9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eKi-67 index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.02 (1.00-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.01 (0.98-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eTumor diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.78 (0.62-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.61 (0.44-0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eNME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.80 (1.26-6.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.20 (1.01-10.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMultifocal disease present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.80 (0.39-0.1.66）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDense tissue type present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.40 (1.60-12.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.89 (0.79-10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMicrocalcification present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.72 (0.35-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eIrregular margins present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.52 (0.25-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eLobulation present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.50 (0.22-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eSpiculation present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.52 (0.26-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eIncreased vascularity present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.71 (0.33-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003ePhase I enhancement rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.04 (0.95-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003ePhase II enhancement rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.04 (0.94-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI, confidence interval; ER, estrogen receptor; OR, odds ratio; PR, progesterone receptor; HER-2, human epidermal growth factor receptor 2; NME, non-mass enhancement; pCR, pathologic complete response.\u003c/p\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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4975514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4975514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCone-beam breast computed tomography (CBBCT) can provide detailed information about breast tissue, but whether such information can help predict treatment response is unclear.\u003c/p\u003e\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo develop a nomogram based on findings from CBBCT as well as conventional clinical variables to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eMedical data were retrospectively analyzed for a consecutive series of women with breast cancer who underwent NAC followed within three months by resection surgery at our hospital between September 2019 and March 2022. Patients were randomized into a development cohort and validation cohort. A nomogram to predict pCR after chemotherapy was formulated based on uni- and multivariate logistic regression of pre-treatment data from the development cohort, and it was tested against data from the validation cohort. The performance of the nomogram was evaluated in terms of the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 215 breast cancer patients in this study, 69 (32.1%) achieved pCR after NAC. Multivariate logistic regression of the development cohort linked such response independently to absence of estrogen receptor (ER) expression, expression of human epidermal growth factor receptor 2 (HER-2), small tumor diameter and non-mass enhancement (NME) on CBBCT. The resulting nomogram predicted response with AUCs of 0.841 (95% CI: 0.78\u0026ndash;0.90) in the development cohort (n\u0026thinsp;=\u0026thinsp;150) and 0.836 (95% CI: 0.74\u0026ndash;0.94) in the validation cohort (n\u0026thinsp;=\u0026thinsp;65), and it was efficient against data from both cohorts based on calibration curves. Decision curve analysis suggested that the nomogram is clinically useful.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA nomogram incorporating molecular biomarkers and findings from CBBCT may help predict breast cancer patients more likely to respond to NAC.\u003c/p\u003e","manuscriptTitle":"Pre-treatment prediction of response to neoadjuvant chemotherapy in breast cancer patients using a nomogram based on findings from cone-beam breast computed tomography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 07:39:35","doi":"10.21203/rs.3.rs-4975514/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-28T10:37:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-28T04:39:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-28T04:38:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-08-26T05:58:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"326dd176-2900-4869-9393-45d9921be177","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T10:09:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 07:39:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4975514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4975514","identity":"rs-4975514","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}