{"paper_id":"340a5d88-e541-4ee1-820f-66f4d76b61bd","body_text":"Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer | 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 Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer L. M. Janssen, B. B. L. Penning Vries, M. H. A. Janse, E. Wall, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4114099/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2024 Read the published version in Breast Cancer Research and Treatment → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose In this study, we aimed to explore if the combination of tumor infiltrating lymphocytes (TILs) and change in tumor load on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) leads to better assessment of response to neoadjuvant chemotherapy (NAC) in patients with breast cancer, compared to either alone. Methods In 190 NAC treated patients, MRI scans were performed before and at the end of treatment. The percentage of stromal TILs (%TILs) was assessed in pre-NAC biopsies according to established criteria. Prediction models were developed with linear regression by least absolute shrinkage and selection operator (LASSO) and cross validation (CV), with residual cancer burden (RCB) as the dependent variable. Discrimination for pathological complete response (pCR) was evaluated using area under the receiver operating characteristic curves (AUC). We used Cox regression analysis for exploring the association between %TILs and recurrence-free survival (RFS). Results Fifty-one patients reached pCR. In all patients, the %TILs model and change in MRI tumor load model had an estimated CV AUC of 0.69 (95% confidence interval (CI) 0.53–0.78) and 0.69 (95%CI 0.61–0.79), respectively, whereas a model combining the variables resulted in an estimated CV AUC of 0.75 (95% CI 0.66–0.83). In the group with tumors that were ER positive and HER2 negative (ER+/HER2-) and in the group with tumors that were either triple negative or HER2 positive (TN&HER2+) separately, the combined model reached an estimated CV AUC of 0.72 (95%CI 0.60–0.88) and 0.70(95%CI 0.59–0.82), respectively. A significant association was observed between pre-treatment %TILS and RFS (hazard ratio (HR) 0.72 (95% CI 0.53–0.98), for every standard deviation increase in %TILS, p = 0.038). Conclusion The combination of TILs and MRI is informative of response to NAC in patients with both ER+/HER2- and TN&HER2 + tumors. Breast cancer tumor infiltrating tumor cells magnetic resonance imaging neoadjuvant chemotherapy pathological complete response Figures Figure 1 Figure 2 Introduction The neoadjuvant treatment approach for breast cancer involves systemic therapy followed by a post-neoadjuvant phase consisting of surgery and/or radiotherapy and/or systemic therapy such as endocrine treatment. Neoadjuvant chemotherapy (NAC) with the tumor in situ allows tumor-response monitoring in vivo using, for instance, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After surgery, the actual response of the tumor is established from the excised tissue, as expressed by pathological complete response (pCR) or the residual cancer burden (RCB), which is associated with survival ( 1 – 3 ). Unfortunately NAC often comes with side effects, some of them long-lasting ( 4 , 5 ). Breast surgery has its own side-effects such as unsatisfactory cosmetic outcome, which is in turn correlated with reduced quality of life ( 6 , 7 ). Reducing these side effects could be possible in patients with tumors sensitive to chemotherapy, where a good response might also be achieved with less intensive NAC, and surgery could perhaps be omitted or postponed. In order to safely select patients for (participation in clinical trials on) de-escalation of NAC or surgery, methods for accurate prediction of response to NAC are essential. Although many efforts have been made to develop new methods, both invasive and non-invasive, none have yet been considered adequate to incorporate in clinical practice. In the post-neoadjuvant phase following surgery (and in most cases radiotherapy), the question on who is to benefit from additional systemic treatment also cannot be answered with full confidence yet. Efforts have been made to develop methods to predict patient survival based on the tumor and patient characteristics such as the Nottingham Prognostic index or PREDICT, and gene expression tests like Oncotype DX, MammaPrint, EndoPredict and Breast Cancer Index ( 8 , 9 ). The majority of these methods has, however, been based on patients who received breast surgery as initial treatment and their role following NAC has not been fully established yet ( 2 , 3 ). There is thus still a clinical need for further refinement of post-neoadjuvant treatment decisions. Many known predictors for response to NAC and post-neoadjuvant prognosis relate to tumor characteristics like estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status ( 2 ), tumor grade ( 10 )) and TNM stage( 11 ). In addition, the immunogenic microenvironment of the tumor plays a role in sensitivity to treatment. A high number of tumor infiltrating lymphocytes (TILs) correlates with higher response rates after NAC and better prognosis in the HER2 + and triple negative (TN) subtypes with odds ratios (ORs) of 2.54 (95% CI 1.50–4.29) and 3.67 (95% CI 1.93–6.97) for pCR, respectively)( 12 ). TILs are usually less abundant in ER+/HER2- breast cancer, and conflicting results have been reported about the relationship with response to NAC and prognosis after NAC ( 13 ). Because the percentage of TILs is easily scored on hematoxylin and eosin (HE) slides of a diagnostic tumor biopsy, additional research to evaluate its predictive value in the ER+/HER2- groups is warranted. To improve prediction of response, an appealing approach is to combine information from different available sources. One study has suggested the potential of TILs added to pre-treatment radiomics of DCE-MRI to better predict response to NAC ( 14 ). Although promising, this was a relatively small study in only TN breast cancer patients. Hence, the potential of this combination needs further confirmation in TN breast cancer, whereas its value is yet to be explored in the other breast cancer subtypes. Hence the aim of this study is to explore if the combination of TILs and DCE-MRI improves the assessment of response to NAC and if TILs enable stratification of prognosis in the post-neoadjuvant phase in the whole group and two subgroups of patients: those with tumors that were ER positive and HER2 negative (ER+/HER2-) and those with tumors that were either triple negative or HER2 positive (TN&HER2+). Methods Patients Two patient cohorts were combined in this study. Cohort A consisted of patients with stage 1–3 invasive breast cancer of any subtype treated with NAC followed by surgery at the University Medical Center Utrecht between January 1st 2011 and December 1st 2019. Patients with oligometastatic disease treated with curative intent were also included. Patients were excluded if they received less than two cycles of NAC, or if biopsies nor MR images were available. All patients with an indication for adjuvant endocrine therapy were offered this treatment. Cohort B consisted of patients with invasive breast cancer from a prospective multicenter NAC study, running from 2020–2022. All patients signed informed consent before enrollment. Inclusion criteria were: female patients aged 18 years or older, histologically proven invasive breast carcinoma and planned to receive NAC. Exclusion criteria were grade 1 estrogen receptor (ER)-positive and HER2-negative breast cancer, inflammatory breast cancer, distant metastases on positron emission tomography/computed tomography (PET/CT), prior ipsilateral breast cancer < 5 years ago, other active malignant diseases in the past 5 years (excluding squamous cell or basal cell carcinoma of the skin), pregnancy or lactation and contra-indications for MRI. All patients underwent NAC according to Dutch guidelines ( 15 ) and were offered adjuvant endocrine therapy if indicated. The potential of MRI features of cohort B to assess response to NAC in combination with liquid biopsies has been reported previously( 16 ) . Pathological evaluation The percentage of stromal TILs in the pre-treatment biopsies and surgical resection specimen were assessed by two experienced breast pathologists (RS, PvD), following the International TILs Working Group guideline ( 17 ). This resulted in a percentage of the stromal tumor bed occupied by TILs (here-after %TILs). The pathologists were blinded to the outcome and non-pathologic predictors during %TILs assessment. The residual cancer burden (RCB) ( 1 ) was assessed by an experienced breast pathologist (PvD) using the calculator provided by the MD Anderson website in both cohorts ( 18 ). The pathologist was blinded to the %TILs and non-pathology predictors during RCB assessment. For cohort A, the tumor grade (according to the Nottingham modification of the Bloom and Richardson method ( 6 , 7 )) was extracted from the pathology report. If the grade was not available from the report, the biopsy was reassessed for grade (by PvD). Pre-treatment ER, HER2 and nodal status were extracted from the pathology reports as well. For cohort B, central revision of all biopsies was performed (by PvD). Positive nodal status was established by image guided lymph node fine needle aspiration or biopsy, or sentinel node procedure prior to NAC. PCR was defined as ypT0/isN0. MR imaging MRI scans were performed before start of treatment in both cohorts. For the patients from cohort A, the MRI that was performed closest to surgery was used as the end-of-treatment MRI. For the patients from cohort B, the end-of-treatment MRI scan was performed after NAC was completed. MRI scans in cohort A were performed at 1.5T or 3T, while all scans in cohort B were performed at 3T. In cohort A, semi-automated delineation of the breast lesions was performed according to previously reported method based on histopathology-validated region growing ( 19 ). In short, seed points were placed by an experienced biomedical engineer (MHAJ) in the lesions based on clinical reporting and verified by a radiologist. From this seed point, automated constrained volume growing took place based on contrast uptake. Small manual corrections were made to remove erroneous segmentations of vessels. In cohort B, a previously-validated deep learning-based approach was employed based on the nnU-Net framework( 20 ). Only segmentations in the breast with the biopsy proven tumor were taken into account. For both the pre-treatment and end-of-treatment scans, the following five features were calculated: 1) number of lesions, defined as the number of lesions that were delineated 2) total lesion volume, in mm³ 3) mean lesion volume, defined as total lesion volume divided by the number of lesions 4) total largest diameter, defined as the largest diameter spanning all lesions 5) sum of the largest diameters, defined as the sum of the largest diameters of individual lesions. Statistical analysis In the data pre-processing step, a variable was created for the relative difference in each of the five MRI tumor load features, by dividing the end-of-treatment feature value by the pre-treatment feature value (hereafter: change in MRI tumor load). The MRI variables and %TILs values were transformed into variables with normal-shaped distributions using a Box-Cox procedure. To be able to use cases with incomplete data in the model development and evaluation, we imputed missing values. For cohort A and B, if the end-of-treatment tumor load features were missing and they were available at an earlier point (from a scan during treatment), the earlier time-point feature values were used for imputation by last observation carried forward. If the features were only available at one timepoint, the sample mean was used for the change in tumor load features. The %TILs and RCB were imputed with the sample mean. Different prediction models were developed to explore the possible additional value of predictors: a model with only %TILs, a model with only change in MRI tumor load, and a third model combining change in MRI tumor load and %TILs. The individual %TILs and change in MRI tumor load models were developed and evaluated in the whole patient population, and then evaluated in the ER+/HER2- and triple negative (TN)/HER2 + subgroups separately. Each model was a (main effects) linear regression model with RCB as the dependent variable, fit using L1-penalised maximum likelihood estimation (LASSO) with the penalty parameter set at the value that yielded the lowest mean squared error in an inner-loop 10-fold cross validation scheme. To estimate the expected out-of-sample performance of the various models in terms of discrimination, we used an additional outer-loop cross validation (CV) with 5 folds and 10 repeats. Discrimination for pCR (RCB 0) vs. residual disease ( RCB > 0) was evaluated using receiver operating characteristic (ROC) curves and, in particular, the area under the ROC curve (AUC). 95% confidence intervals (CI) were estimated by bootstrapping the original data and performing repeated cross-validation in each bootstrap sample. For estimation of the median follow-up time after NAC, the reverse Kaplan Meier method was used. For exploring the association between %TILs and recurrence-free survival (RFS, as previously defined( 21 )), we used Cox regression analysis with the box-cox transformed %TILs as the explaining variable, calculating hazard ratios (HR). In order to create Kaplan Meier curves, the original %TILs values were stratified in two predefined groups of 1–10% and > 10% TILs, corresponding to the low vs. intermediate + high groups of a large pooled analysis( 22 ). We merged the intermediate and high subgroups of TILs because of the expected lower TILs counts in the ER+/HER2- group. All statistical analysis were performed in R software version 4.2.2. Results A total of 190 patients were included in this study (129 in cohort A, 61 in B) of which 106 were ER+/HER2-, 40 ER-/HER2- and 44 ER+-/HER2+. All patients underwent surgery after NAC, 51 patients reached pCR (Table 1 , Supplementary Tables 1 and 2). Median follow up time after NAC was 58 months. There were a total of 31 RFS events. Table 1 Patient and tumor characteristics of breast cancer patients treated with NAC that were included in the study, overall and according to pCR vs. residual disease, %TILs and cohort. cT stage: clinical tumor stage according to the American Joint Committee on Cancer (AJCC) staging system. pCR: pathological complete response Total (N = 190) %TILs 1–10* (N = 122) %TILs > 10* (N = 68) Cohort A (N = 129) Cohort B (N = 61) Age (years) Median [Min, Max] 50 [25–78] 52.50 [25.00, 78.00] 48.50 [25.00, 71.00] 50.00 [25.00, 78.00] 50.00 [25.00, 72.00] Histology (%) Invasive carcinoma NST 132 (69.5) 79 (64.8) 53 (77.9) 79 (61.2) 53 (86.9) Ductolobular carcinoma 33 (17.4) 26 (21.3) 7 (10.3) 31 (24.0) 2 ( 3.3) Lobular carcinoma 20 (10.5) 15 (12.3) 5 ( 7.4) 15 (11.6) 5 ( 8.2) Other 5 ( 2.6) 2 ( 1.6) 3 ( 4.4) 4 ( 3.1) 1 ( 1.6) Grade (%) 1 17 (8.9) 14 (11.5) 3 ( 4.4) 17 (13.2) 0 ( 0.0) 2 92 (48.4) 64 (52.5) 28 (41.2) 68 (52.7) 24 (39.3) 3 81 (42.6) 44 (36.1) 37 (54.4) 44 (34.1) 37 (60.7) Missing 2 0 2 2 0 IHC subtype (%) ER-/HER2- 40 (21.1) 22 (18.0) 18 (26.5) 19 (14.7) 21 (34.4) ER+-/HER2+ 44 (23.2) 25 (20.5) 19 (27.9) 27 (20.9) 17 (27.9) ER+/HER2- 106 (55.8) 75 (61.5) 31 (45.6) 83 (64.3) 23 (37.7) cT stage (%) T1 30 (15.9) 19 (15.7) 11 (16.2) 19 (14.7) 11 (18.3) T2 111 (58.7) 75 (62.0) 36 (52.9) 74 (57.4) 37 (61.7) T3 37 (19.6) 21 (17.4) 16 (23.5) 25 (19.4) 12 (20.0) T4 a-b 11 (5.8) 6 ( 5.0) 5 ( 7.4) 11 ( 8.5) 0 ( 0.0) Missing 1 1 0 0 1 Nodal metastases (%) Absent 90 (47.4) 60 (49.2) 30 (44.1) 67 (51.9) 23 (37.7) Present 100 (52.6) 62 (50.8) 38 (55.9) 62 (48.1) 38 (62.3) %TILs 1–10 122 (64.2) . . 76 (58.9) 46 (75.4) > 10 68 (35.8) . . 53 (41.1) 15 (24.6) Neoadjuvant treatment (%) FEC 2 ( 1.1) 2 ( 1.6) 0 ( 0.0) 2 ( 1.6) 0 ( 0.0) FEC-DOC 79 (41.6) 53 (43.4) 26 (38.2) 79 (61.2) 0 ( 0.0) DOC + CP 3 ( 1.6) 1 ( 0.8) 2 ( 2.9) 3 ( 2.3) 0 ( 0.0) TAC 1 ( 0.5) 0 ( 0.0) 1 ( 1.5) 1 ( 0.8) 0 ( 0.0) AC-P 42 (22.1) 28 (23.0) 14 (20.6) 16 (12.4) 26 (42.6) AC-P + trastuzumab 24 (12.6) 11 ( 9.0) 13 (19.1) 24 (18.6) 0 ( 0.0) AC-P + carboplatin 18 ( 9.5) 12 ( 9.8) 6 ( 8.8) 0 ( 0.0) 18 (29.5) Paclitaxel + trastuzumab 2 ( 1.1) 2 ( 1.6) 0 ( 0.0) 1 ( 0.8) 1 ( 1.6) PTCP 18 ( 9.5) 12 ( 9.8) 6 ( 8.8) 2 ( 1.6) 16 (26.2) AC + 2x intensified CP, thiotepa and carboplatin 1 ( 0.5) 1 ( 0.8) 0 ( 0.0) 1 ( 0.8) 0 ( 0.0) Relative change on MRI* median [min, max] Number of lesions 63.7 [0.0, 400.0] 66. 7 [0.0, 400.0] 50.0 [0.0, 250.0] 66.7 [0.0, 200.0] 50.0 [0.0, 400.0] Total volume 4.5 [0.0, 221.0] 6.7 [0.0, 115.6] 1.6 [0.0, 221.0] 6.0 [0.0, 221.0] 3.8 [0.0, 63.2] Mean volume 6.5 [0.0, 221.0] 9.0 [0.0, 97.2] 1.6 [0.0, 221.0] 6.5 [0.0, 221.0] 6.6 [0.0, 71.4] Total largest diameter 44.2 [0.0, 425.3] 50.6 [0.0, 425.3] 23.5 [0.0, 289.4] 426 [0.0, 425.3] 47.1 [0.0, 328.1] Sum largest diameter 34.9 [0.0, 192.9] 41.6 [0.0, 192.9] 17.9 [0.0, 135.8] 41.3 [0.0, 192.9] 31.9 [0.0, 135.8] Surgery type (%) Lumpectomy 99 (52.1) 66 (54.1) 33 (48.5) 74 (57.4) 25 (41.0) Mastectomy 91 (47.9) 56 (45.9) 35 (51.5) 55 (42.6) 36 (59.0) RCB * Median [min, max] 1.54 [0.00, 4.22] 1.61 [0.00, 3.72] 1.23 [0.00, 4.22] 1.55 [0.00, 4.22] 1.22 [0.00, 3.42] FEC: 5-fluorouracil, epirubicin, cyclophosphamide, DOC: docetaxel, CP: cyclophosphamide, AC-P: Doxorubicin, cyclophosphamide and paclitaxel. PTCP: Paclitaxel, trastuzumab, carboplatin and pertuzumab, TAC: Docetaxel, doxorubicin and cyclophosphamide, AC: Doxorubicin, cyclophosphamide. * Values after imputation There were a total of 13% missing values for the change in tumor load MRI variables and 15% for the %TILs values. RCB was missing in 2 cases. See Supplementary Tables 1 and 2 for missing data per cohort. Proportion of missing data was comparable among the IHC subtypes. Assessment of response to NAC Figure 1 depicts the ROC curves and corresponding (cross validated) AUC of each of the models. Coefficients can be found in Supplementary Table 3. A prediction model containing the change in MRI tumor load reached an estimated CV AUC of 0.69 (95% CI 0.61–0.79) in all patients, and a model with only %TILs had an estimated CV AUC of 0.69 (95% CI 0.53–0.78) A prediction model combining %TILs and change in MRI tumor load had an estimated CV AUC of 0.75(95% CI 0.67–0.83). The change in MRI tumor load model evaluated in the ER+/HER2- patient group yielded an estimated CV AUC of 0.67(95% CI 0.51–0.84), the %TILs-only model an estimated CV AUC of 0.68 (95% CI 0.50–0.82), while the combined model had an estimated CV AUC of 0.72 (95%CI 0.60–0.88). For the TN&HER2 + subgroup, the change in MRI tumor load model had an estimated CV AUC of 0.67 (95% CI 0.56–0.79), the %TILs only model an estimated CV AUC of 0.63 (95% CI 0.49–0.74), while the combined model had an estimated CV AUC of 0.70(95% CI 0.59–0.82). Explorative analysis of TILs vs. event-free survival %TILs was significantly associated with RFS in all patients (HR 0.72(95% CI 0.53–0.98), for every standard deviation increase in %TILS, p = 0.038). This association does not appear substantially different in the two subgroups (HR 0.68 (95% CI 0.44–1.074), p = 0.10 in the ER+/HER2- group and HR 0.72 (95% CI 0.44–1.19), p = 0.20 in the TN&HER2 + group). Figure 2 shows the survival curves of patients with %TILs 1–10 vs. >10 in each of the groups. %TILs in the resection specimen was also evaluated but was not available in all patients resulting in insufficient data to properly assess the association with RFS. Discussion In this multicenter study, we explored the combination of %TILs and change in tumor load on DCE-MRI to assess response to NAC in patients with ER+/HER2- and TN&HER2 + breast cancer. A higher CV AUC was observed for the combination of %TILs and change in tumor load on MRI compared to either one alone in the whole group ((0.75 (95% CI 0.67–0.83) vs. 0.69 for %TILS-only (95% CI 0.53–0.78) and 0.69 (95%CI 0.61–0.79) for change in MRI tumor load only). This was also observed in the ER+/HER2- group ((0.72 (95%CI 0.60–0.88) vs. 0.68 (95% CI 0.50–0.82) and vs. 0.67 (95% CI 0.51–0.82), as well as in the TN&HER2 + group ((0.70 (95% CI 0.59–0.82) vs. 0.63 (95% CI 0.49–0.74) and vs. 0.67 (95% CI 0.56–0.79). The difference in observed discriminative ability should, however, be interpreted with caution given the wide confidence intervals. There is a large need for improvement of response prediction, before clinical trials on postponing or omitting surgery after NAC have a good chance of succeeding ( 23 ). Our work suggests that %TILs and MRI may hold complementary information and could be a useful combined biomarker for response to NAC in different breast cancer subtypes. TILs have been shown by others to be correlated to pCR in the HER2 + and TN subtypes, with higher TILs relating to higher pCR rates ( 12 , 22 ). In the ER+/HER2- subtype, the literature is inconclusive. A large pooled analysis by Denkert et al. showed a significant positive correlation between TILs and pCR in the ER+/HER2- subtype( 22 ). A different meta-analysis and some other smaller studies did, however, not find this correlation ( 12 , 13 , 24 – 26 ). TILs are reported to be less frequent in ER+/HER2- breast cancer compared to the other subtypes ( 27 ), which makes it less likely to find a correlation in smaller groups. We found an association between TILs and response to NAC as measured by RCB in the whole group of patients. One study found significant correlations between RCB classes and TIL CD8/FOXP3 ratio in TN breast cancer ( 28 ). A different study by Elmahs et al. did not find a correlation between TILs and RCB class, perhaps due to small sample size ( 29 ). Immunotherapy is a topic of increasing interest in breast cancer treatment, initially investigated in the metastatic setting, followed by the neoadjuvant setting. In the TN subtype, the combination of immunotherapy and NAC was shown to improve pCR in 2020( 30 ). In TN patients treated with this combination, higher sTILs were associated with higher pCR rates ( 31 – 33 ). More recently, promising results for the combination of immunotherapy and NAC have been proposed in the luminal subtype as well ( 34 , 35 ). Personalizing treatment and not giving more than necessary is of special importance in the light of high costs associated with immunotherapy. In a recent study by Loi et. al., presented at SABCS 2023, higher pCR rates were seen with increasing sTILs in patients with high risk luminal breast cancer treated with NAC and nivolumab( 36 ). Models like the one presented in this study could potentially aid clinical decision making in treatment with the combination of NAC and immunotherapy in the future. They should however be evaluated in a population treated with this regimen. With regard to the prognostic value of TILs, we found that higher %TILs in biopsy is associated with better RFS after NAC in the whole cohort. This suggests that %TILs could also be useful for post NAC decision making, although its role in relation to other prognostic factors was not investigated here due to too few events. High TILs have been reported to be correlated to better prognosis in the TN and HER2 + subtypes ( 12 , 22 , 37 ). In the ER+/HER2- group, the pooled analysis by Denkert et. al. reported low TILs (0–10%) to be correlated with improved disease free survival, in contrast with our results( 22 ). The meta-analysis by Li et. al. reported no correlation between TILs and survival( 12 ). Our work thus contributes to the growing body of research on the prognostic role of TILs in breast cancer. We did not have enough data to evaluate the relationship with TILs in the residual tumor to RFS, but work is underway to incorporate TILs in the residual tumor after NAC in the RCB to further stratify post-neoadjuvant prognosis( 38 ).Since MRI is more accurate in evaluating response to NAC compared to mammography, ultrasound and physical examination, it is widely used in clinical practice ( 39 – 41 ). Radiological assessment alone is, however, not accurate enough to guide treatment decisions ( 42 ). A (semi-) automated method for evaluating response to NAC could be of interest, since manual measurement by RECIST is associated with intra- and interobserver variability ( 43 – 45 ). Tissue biopsy is always a part of the diagnostic pre-treatment work-up and assessing TILs in the biopsy is quick and easily implemented, possibly even more so when artificial intelligence algorithms are deployed ( 46 ). The combination of TILs and computer extracted MRI features may therefore be an efficient use of information that is available from the clinical workflow without additional (invasive) procedures. Our results suggest the complementary value of these different data sources in assessing response to NAC, which could ultimately help in sparing patients unnecessary treatment. Our study has several limitations. First, there was no independent cohort to perform external validation of the developed models. Second, due to limited sample size, we were unable to account for relevant predictors such as treatment regimen and nodal status, or to evaluate the HER2 + and TN subtypes separately. Third, our two cohorts contain patients from different periods in time, which resulted in different treatment regimens that may not reflect current clinical practice. Additionally, for cohort A, not all biopsies were centrally revised for ER, HER2 and nodal status. This could result in unwanted interobserver variability in these variables, which is however a reflection of clinical practice. Lastly, the MRI processing differed between cohort A and B. In theory, this could have impacted the results, although the methods have been shown to lead to highly correlated results( 20 ). In conclusion, our results show that the combination of TILs and change in tumor load on MRI is informative of response after NAC overall, as well as in the ER+/HER2- and TN&HER2 + groups separately. This could be of interest for clinical trials on de-escalating surgical intervention. More work is, however, needed to reduce uncertainty and improve accuracy by modifying for other predictors as well. Declarations Competing Interests All authors have declared no conflict of interest. Ethics approval Cohort A: Requirement for informed consent and ethical review was waived by the institutional review board (Medical Research Ethics Committee Utrecht, no. 19–245). Funding This project was funded in part by the European Union Horizon 2020 research and innovation program [grant number 755333] Author Contribution L.M. Janssen, S. G. Elias, E. van der Wall, P. J. van Diest and K. G. A. Gilhuijs contributed to the study design. L.M. Janssen collected and curated the clinical data and wrote the manuscript. L.M. Janssen and B. B. L. Penning de Vries conducted the statistical analysis. S. G. Elias and B. B. L. Penning de Vries provided biostatistical and epidemiological support. M. H. A. Janse provided quantitative MRI data. P. J. van Diest performed the pathology revision. R. Salgado performed the TILs assessment. All authors revised the manuscript and approved the final version. Data availability Datasets and R code used for analysis are available from the corresponding author on reasonable request. 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J Clin Oncol 40(16):1816–1837 Candido Dos Reis FJ, Wishart GC, Dicks EM, Greenberg D, Rashbass J, Schmidt MK et al (2017) An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation. Breast cancer research: BCR 19(1):58 Lips EH, Mulder L, de Ronde JJ, Mandjes IA, Koolen BB, Wessels LF et al (2013) Breast cancer subtyping by immunohistochemistry and histological grade outperforms breast cancer intrinsic subtypes in predicting neoadjuvant chemotherapy response. Breast Cancer Res Treat 140(1):63–71 Giuliano AE, Connolly JL, Edge SB, Mittendorf EA, Rugo HS, Solin LJ et al (2017) Breast Cancer-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 67(4):290–303 Li S, Zhang Y, Zhang P, Xue S, Chen Y, Sun L, Yang R (2022) Predictive and prognostic values of tumor infiltrating lymphocytes in breast cancers treated with neoadjuvant chemotherapy: A meta-analysis. Breast (Edinburgh Scotland) 66:97–109 Goldberg J, Pastorello RG, Vallius T, Davis J, Cui YX, Agudo J et al (2021) The Immunology of Hormone Receptor Positive Breast Cancer. Front Immunol. ;12 Jimenez JE, Abdelhafez A, Mittendorf EA, Elshafeey N, Yung JP, Litton JK et al (2022) A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer. Eur J Radiol 149:110220 (IKNL) IkN Landelijke richtlijn Borstkanker 2020 [Available from: https://richtlijnendatabase.nl Janssen LM, Janse MHA, Penning de Vries BBL, van der Velden BHM, Wolters-van der Ben EJM, van den Bosch SM et al (2024) Predicting response to neoadjuvant chemotherapy with liquid biopsies and multiparametric MRI in patients with breast cancer. NPJ breast cancer 10(1):10 Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G et al (2015) The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol 26(2):259–271 Center MAC (2018) Residual Cancer Burden Calculator [cited. Available from: http://www3.mdanderson.org/app/medcalc/index.cfm?pagename=jsconvert3 Alderliesten T, Schlief A, Peterse J, Loo C, Teertstra H, Muller S, Gilhuijs K (2007) Validation of semiautomatic measurement of the extent of breast tumors using contrast-enhanced magnetic resonance imaging. Invest Radiol 42(1):42–49 Janse MHA, Janssen LM, van der Velden BHM, Moman MR, Wolters-van der Ben EJM, Kock MCJM et al (2023) Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study. J Magn Reson Imaging 58(6):1739–1749 Hudis CA, Barlow WE, Costantino JP, Gray RJ, Pritchard KI, Chapman JA et al (2007) Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol 25(15):2127–2132 Denkert C, von Minckwitz G, Darb-Esfahani S, Lederer B, Heppner BI, Weber KE et al (2018) Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol 19(1):40–50 van Hemert AKE, van Duijnhoven FH, van Loevezijn AA, Loo CE, Wiersma T, Groen EJ, Peeters M (2023) Biopsy-Guided Pathological Response Assessment in Breast Cancer is Insufficient: Additional Pathology Findings of the MICRA Trial. Ann Surg Oncol Ono M, Tsuda H, Shimizu C, Yamamoto S, Shibata T, Yamamoto H et al (2012) Tumor-infiltrating lymphocytes are correlated with response to neoadjuvant chemotherapy in triple-negative breast cancer. Breast Cancer Res Treat 132(3):793–805 Hwang HW, Jung H, Hyeon J, Park YH, Ahn JS, Im YH et al (2019) A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Breast Cancer Res Treat 173(2):255–266 Russo L, Maltese A, Betancourt L, Romero G, Cialoni D, De la Fuente L et al (2019) Locally advanced breast cancer: Tumor-infiltrating lymphocytes as a predictive factor of response to neoadjuvant chemotherapy. Eur J Surg Oncol 45(6):963–968 Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F et al (2013) Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98. J Clin Oncol 31(7):860–867 Miyashita M, Sasano H, Tamaki K, Chan M, Hirakawa H, Suzuki A et al (2014) Tumor-infiltrating CD8 + and FOXP3 + lymphocytes in triple-negative breast cancer: its correlation with pathological complete response to neoadjuvant chemotherapy. Breast Cancer Res Treat 148(3):525–534 Elmahs A, Mohamed G, Salem M, Omar D, Helal AM, Soliman N (2022) The Impact of Tumor Infiltrating Lymphocytes Densities and Ki67 Index on Residual Breast Cancer Burden following Neoadjuvant Chemotherapy. Int J Breast Cancer 2022:2597889 Schmid P, Cortes J, Pusztai L, McArthur H, Kümmel S, Bergh J et al (2020) Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med 382(9):810–821 Wood SJ, Gao Y, Lee JH, Chen J, Wang Q, Meisel JL, Li X (2024) High tumor infiltrating lymphocytes are significantly associated with pathological complete response in triple negative breast cancer treated with neoadjuvant KEYNOTE-522 chemoimmunotherapy. Breast Cancer Res Treat Loibl S, Untch M, Burchardi N, Huober J, Sinn BV, Blohmer JU et al (2019) A randomised phase II study investigating durvalumab in addition to an anthracycline taxane-based neoadjuvant therapy in early triple-negative breast cancer: clinical results and biomarker analysis of GeparNuevo study. Ann Oncol 30(8):1279–1288 Schmid P, Salgado R, Park YH, Muñoz-Couselo E, Kim SB, Sohn J et al (2020) Pembrolizumab plus chemotherapy as neoadjuvant treatment of high-risk, early-stage triple-negative breast cancer: results from the phase 1b open-label, multicohort KEYNOTE-173 study. Ann Oncol 31(5):569–581 Loi S, Curigliano G, Salgado RF, Romero Diaz RI, Delaloge S, Rojas C et al (2023) LBA20 A randomized, double-blind trial of nivolumab (NIVO) vs placebo (PBO) with neoadjuvant chemotherapy (NACT) followed by adjuvant endocrine therapy (ET) ± NIVO in patients (pts) with high-risk, ER + HER2 – primary breast cancer (BC). Ann Oncol 34:S1259–S60 Cardoso F, McArthur HL, Schmid P, Cortés J, Harbeck N, Telli ML et al (2023) LBA21 KEYNOTE-756: Phase III study of neoadjuvant pembrolizumab (pembro) or placebo (pbo) + chemotherapy (chemo), followed by adjuvant pembro or pbo + endocrine therapy (ET) for early-stage high-risk ER+/HER2– breast cancer. Ann Oncol 34:S1260–S1 Sherene Loi GC, Roberto Salgado Roberto Iván Romero Díaz, Suzette Delaloge CIRG, Marleen Kok, Cristina Saura, Nadia Harbeck EAM, Denise A. Yardley, Lajos Pusztai, Alberto Suárez Zaizar AU, Felipe Ades, Rajalakshmi Chandra, Raheel Nathani MP, Thomas Spires,16 Jenny Qun Wu, Heather McArthur. Biomarker results in high-risk estrogen receptor–positive, human epidermal growth factor receptor 2–negative primary breast cancer following neoadjuvant chemotherapy ± nivolumab: an exploratory analysis of CheckMate 7FL. San Antonio Breast Cancer Symposium; December 5–9 20232023 Martín M, Yoder R, Salgado R, Del Monte-Millán M, Alvarez EL, Echavarría I et al (2024) Tumor-infiltrating lymphocytes refine outcomes in triple-negative breast cancer treated with anthracycline-free neoadjuvant chemotherapy. Clin Cancer Res Dieci MV, Radosevic-Robin N, Fineberg S, van den Eynden G, Ternes N, Penault-Llorca F et al (2018) Update on tumor-infiltrating lymphocytes (TILs) in breast cancer, including recommendations to assess TILs in residual disease after neoadjuvant therapy and in carcinoma in situ: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer. Sem Cancer Biol 52:16–25 Scheel JR, Kim E, Partridge SC, Lehman CD, Rosen MA, Bernreuter WK et al (2018) MRI, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial. AJR American journal of roentgenology. ;210(6):1376-85 Marinovich ML, Macaskill P, Irwig L, Sardanelli F, Mamounas E, von Minckwitz G et al (2015) Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer 15:662 Park J, Chae EY, Cha JH, Shin HJ, Choi WJ, Choi YW, Kim HH (2018) Comparison of mammography, digital breast tomosynthesis, automated breast ultrasound, magnetic resonance imaging in evaluation of residual tumor after neoadjuvant chemotherapy. Eur J Radiol 108:261–268 Janssen LM, den Dekker BM, Gilhuijs KGA, van Diest PJ, van der Wall E, Elias SG (2022) MRI to assess response after neoadjuvant chemotherapy in breast cancer subtypes: a systematic review and meta-analysis. NPJ breast cancer 8(1):107 Beresford MJ, Padhani AR, Taylor NJ, Ah-See ML, Stirling JJ, Makris A et al (2006) Inter- and intraobserver variability in the evaluation of dynamic breast cancer MRI. J Magn Reson Imaging 24(6):1316–1325 Suzuki C, Torkzad MR, Jacobsson H, Åström G, Sundin A, Hatschek T et al (2010) Interobserver and intraobserver variability in the response evaluation of cancer therapy according to RECIST and WHO-criteria. Acta Oncol 49(4):509–514 Karmakar A, Kumtakar A, Sehgal H, Kumar S, Kalyanpur A (2019) Interobserver Variation in Response Evaluation Criteria in Solid Tumors 1.1. Acad Radiol 26(4):489–501 Albusayli R, Graham JD, Pathmanathan N, Shaban M, Raza SEA, Minhas F et al (2023) Artificial intelligence-based digital scores of stromal tumour-infiltrating lymphocytes and tumour-associated stroma predict disease-specific survival in triple-negative breast cancer. J Pathol 260(1):32–42 Additional Declarations No competing interests reported. Supplementary Files Supplementsfinal.docx Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2024 Read the published version in Breast Cancer Research and Treatment → Version 1 posted Editorial decision: Revision requested 15 Apr, 2024 Reviews received at journal 09 Apr, 2024 Reviews received at journal 04 Apr, 2024 Reviewers agreed at journal 27 Mar, 2024 Reviewers agreed at journal 25 Mar, 2024 Reviewers invited by journal 25 Mar, 2024 Submission checks completed at journal 18 Mar, 2024 Editor assigned by journal 18 Mar, 2024 First submitted to journal 16 Mar, 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-4114099\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":280866757,\"identity\":\"eab53f06-6ac0-452e-9bac-e48ee67bce6d\",\"order_by\":0,\"name\":\"L. M. Janssen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"L.\",\"middleName\":\"M.\",\"lastName\":\"Janssen\",\"suffix\":\"\"},{\"id\":280866758,\"identity\":\"4130bb5e-95a2-4a1c-b5ec-f3465a82ff25\",\"order_by\":1,\"name\":\"B. B. L. Penning Vries\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"B.\",\"middleName\":\"B. L. Penning\",\"lastName\":\"Vries\",\"suffix\":\"\"},{\"id\":280866759,\"identity\":\"9e67fc93-778d-4e5e-b4f5-03ce127c8e7f\",\"order_by\":2,\"name\":\"M. H. A. Janse\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"M.\",\"middleName\":\"H. A.\",\"lastName\":\"Janse\",\"suffix\":\"\"},{\"id\":280866760,\"identity\":\"07087c1c-7b61-4c2a-bbb6-d9974aa0357a\",\"order_by\":3,\"name\":\"E. Wall\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Medical Oncology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"E.\",\"middleName\":\"\",\"lastName\":\"Wall\",\"suffix\":\"\"},{\"id\":280866761,\"identity\":\"bad66f4e-858b-4439-858d-5a54544f1541\",\"order_by\":4,\"name\":\"S. G. 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Gilhuijs\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2QsUoDQRCGZ1m4axav3RBJXmGWFBrQd9kjkE4RBOsDYW3urM1bpBLLlYNLE/QBrjDiCxykuWAKJzktApsjZcD9quWf+WaWAfB4jpAoYQnUeK6Bg4bqL6YQIHQr0nLLUpRbhT1tomCrIFDiBCHQVCKF4OIwRSCHG3mNM/haXprnPn5kn4vVyxrOWhWUt5jDuHtlSjUtwoHK5gjDe7eiLCmnKOMpKZwUjUUQdJlBoCHuLTaquGiU0XLYKOF3u0JbfhVNw5stvE2RucBXOnI8oY910rdSTYox72RmIPYp0UOqFvX6In58n4+q+q7sn+QFq1am18NZ4nbokrZ5Cb1TEO7+HUJ7QJPH4/H8R34Aa4BZ0PPi+DwAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Kenneth\",\"middleName\":\"G. A.\",\"lastName\":\"Gilhuijs\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-03-16 16:59:19\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4114099/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4114099/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s10549-024-07484-7\",\"type\":\"published\",\"date\":\"2024-09-16T15:57:46+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":53194035,\"identity\":\"9f6009f6-d3f3-4e37-b8e9-d1d64691beaa\",\"added_by\":\"auto\",\"created_at\":\"2024-03-21 18:10:11\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":125303,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eROC curves for discriminating between pCR (RCB 0) and residual disease (RCB \\u0026gt; 0) for the different prediction models. The black line represents the mean curve over all CV loops. The dotted lines represent the 95% confidence intervals. AUC = cross-validated area under the curve.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4114099/v1/8eda5b7f52aadb5d1432a521.png\"},{\"id\":53194038,\"identity\":\"216f07e1-2730-4cce-a82c-3daaa52bb47a\",\"added_by\":\"auto\",\"created_at\":\"2024-03-21 18:10:11\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":184317,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eKaplan Meier curve for recurrence free survival stratified to TILs in biopsy of \\u0026gt;10% (red line) vs. 1-10% (blue line). A) RFS for all patients by TILs in biopsy. B) RFS for ER+/HER2- patients by TILs in biopsy. C) RFS for TN\\u0026amp;HER2+ patients by TILs in biopsy\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4114099/v1/7ff82b219029654c9f901e66.jpeg\"},{\"id\":65109741,\"identity\":\"3c732979-9078-407d-b124-90c22e3bd646\",\"added_by\":\"auto\",\"created_at\":\"2024-09-23 17:48:58\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":918454,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4114099/v1/d7b8f211-20e9-4070-a1d9-99fccb0413f6.pdf\"},{\"id\":53194037,\"identity\":\"a993f7bd-b58c-4d66-b520-103788d0e2f8\",\"added_by\":\"auto\",\"created_at\":\"2024-03-21 18:10:11\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17415,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementsfinal.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4114099/v1/48defc713ca0147bb4bab0b4.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe neoadjuvant treatment approach for breast cancer involves systemic therapy followed by a post-neoadjuvant phase consisting of surgery and/or radiotherapy and/or systemic therapy such as endocrine treatment. Neoadjuvant chemotherapy (NAC) with the tumor \\u003cem\\u003ein situ\\u003c/em\\u003e allows tumor-response monitoring \\u003cem\\u003ein vivo\\u003c/em\\u003e using, for instance, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After surgery, the actual response of the tumor is established from the excised tissue, as expressed by pathological complete response (pCR) or the residual cancer burden (RCB), which is associated with survival (\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Unfortunately NAC often comes with side effects, some of them long-lasting (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). Breast surgery has its own side-effects such as unsatisfactory cosmetic outcome, which is in turn correlated with reduced quality of life (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Reducing these side effects could be possible in patients with tumors sensitive to chemotherapy, where a good response might also be achieved with less intensive NAC, and surgery could perhaps be omitted or postponed. In order to safely select patients for (participation in clinical trials on) de-escalation of NAC or surgery, methods for accurate prediction of response to NAC are essential. Although many efforts have been made to develop new methods, both invasive and non-invasive, none have yet been considered adequate to incorporate in clinical practice.\\u003c/p\\u003e \\u003cp\\u003eIn the post-neoadjuvant phase following surgery (and in most cases radiotherapy), the question on who is to benefit from additional systemic treatment also cannot be answered with full confidence yet. Efforts have been made to develop methods to predict patient survival based on the tumor and patient characteristics such as the Nottingham Prognostic index or PREDICT, and gene expression tests like Oncotype DX, MammaPrint, EndoPredict and Breast Cancer Index (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). The majority of these methods has, however, been based on patients who received breast surgery as initial treatment and their role following NAC has not been fully established yet (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). There is thus still a clinical need for further refinement of post-neoadjuvant treatment decisions.\\u003c/p\\u003e \\u003cp\\u003eMany known predictors for response to NAC and post-neoadjuvant prognosis relate to tumor characteristics like estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e), tumor grade (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e)) and TNM stage(\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). In addition, the immunogenic microenvironment of the tumor plays a role in sensitivity to treatment. A high number of tumor infiltrating lymphocytes (TILs) correlates with higher response rates after NAC and better prognosis in the HER2\\u0026thinsp;+\\u0026thinsp;and triple negative (TN) subtypes with odds ratios (ORs) of 2.54 (95% CI 1.50\\u0026ndash;4.29) and 3.67 (95% CI 1.93\\u0026ndash;6.97) for pCR, respectively)(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). TILs are usually less abundant in ER+/HER2- breast cancer, and conflicting results have been reported about the relationship with response to NAC and prognosis after NAC (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). Because the percentage of TILs is easily scored on hematoxylin and eosin (HE) slides of a diagnostic tumor biopsy, additional research to evaluate its predictive value in the ER+/HER2- groups is warranted.\\u003c/p\\u003e \\u003cp\\u003eTo improve prediction of response, an appealing approach is to combine information from different available sources. One study has suggested the potential of TILs added to pre-treatment radiomics of DCE-MRI to better predict response to NAC (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Although promising, this was a relatively small study in only TN breast cancer patients. Hence, the potential of this combination needs further confirmation in TN breast cancer, whereas its value is yet to be explored in the other breast cancer subtypes.\\u003c/p\\u003e \\u003cp\\u003eHence the aim of this study is to explore if the combination of TILs and DCE-MRI improves the assessment of response to NAC and if TILs enable stratification of prognosis in the post-neoadjuvant phase in the whole group and two subgroups of patients: those with tumors that were ER positive and HER2 negative (ER+/HER2-) and those with tumors that were either triple negative or HER2 positive (TN\\u0026amp;HER2+).\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatients\\u003c/h2\\u003e \\u003cp\\u003eTwo patient cohorts were combined in this study. Cohort A consisted of patients with stage 1\\u0026ndash;3 invasive breast cancer of any subtype treated with NAC followed by surgery at the University Medical Center Utrecht between January 1st 2011 and December 1st 2019. Patients with oligometastatic disease treated with curative intent were also included. Patients were excluded if they received less than two cycles of NAC, or if biopsies nor MR images were available. All patients with an indication for adjuvant endocrine therapy were offered this treatment.\\u003c/p\\u003e \\u003cp\\u003eCohort B consisted of patients with invasive breast cancer from a prospective multicenter NAC study, running from 2020\\u0026ndash;2022. All patients signed informed consent before enrollment. Inclusion criteria were: female patients aged 18 years or older, histologically proven invasive breast carcinoma and planned to receive NAC. Exclusion criteria were grade 1 estrogen receptor (ER)-positive and HER2-negative breast cancer, inflammatory breast cancer, distant metastases on positron emission tomography/computed tomography (PET/CT), prior ipsilateral breast cancer\\u0026thinsp;\\u0026lt;\\u0026thinsp;5 years ago, other active malignant diseases in the past 5 years (excluding squamous cell or basal cell carcinoma of the skin), pregnancy or lactation and contra-indications for MRI. All patients underwent NAC according to Dutch guidelines (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e) and were offered adjuvant endocrine therapy if indicated. The potential of MRI features of cohort B to assess response to NAC in combination with liquid biopsies has been reported previously(\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePathological evaluation\\u003c/h2\\u003e \\u003cp\\u003eThe percentage of stromal TILs in the pre-treatment biopsies and surgical resection specimen were assessed by two experienced breast pathologists (RS, PvD), following the International TILs Working Group guideline (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). This resulted in a percentage of the stromal tumor bed occupied by TILs (here-after %TILs). The pathologists were blinded to the outcome and non-pathologic predictors during %TILs assessment. The residual cancer burden (RCB) (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) was assessed by an experienced breast pathologist (PvD) using the calculator provided by the MD Anderson website in both cohorts (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). The pathologist was blinded to the %TILs and non-pathology predictors during RCB assessment. For cohort A, the tumor grade (according to the Nottingham modification of the Bloom and Richardson method (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e)) was extracted from the pathology report. If the grade was not available from the report, the biopsy was reassessed for grade (by PvD). Pre-treatment ER, HER2 and nodal status were extracted from the pathology reports as well. For cohort B, central revision of all biopsies was performed (by PvD). Positive nodal status was established by image guided lymph node fine needle aspiration or biopsy, or sentinel node procedure prior to NAC. PCR was defined as ypT0/isN0.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMR imaging\\u003c/h2\\u003e \\u003cp\\u003eMRI scans were performed before start of treatment in both cohorts. For the patients from cohort A, the MRI that was performed closest to surgery was used as the end-of-treatment MRI. For the patients from cohort B, the end-of-treatment MRI scan was performed after NAC was completed. MRI scans in cohort A were performed at 1.5T or 3T, while all scans in cohort B were performed at 3T. In cohort A, semi-automated delineation of the breast lesions was performed according to previously reported method based on histopathology-validated region growing (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). In short, seed points were placed by an experienced biomedical engineer (MHAJ) in the lesions based on clinical reporting and verified by a radiologist. From this seed point, automated constrained volume growing took place based on contrast uptake. Small manual corrections were made to remove erroneous segmentations of vessels. In cohort B, a previously-validated deep learning-based approach was employed based on the nnU-Net framework(\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). Only segmentations in the breast with the biopsy proven tumor were taken into account. For both the pre-treatment and end-of-treatment scans, the following five features were calculated: 1) number of lesions, defined as the number of lesions that were delineated 2) total lesion volume, in mm\\u0026sup3; 3) mean lesion volume, defined as total lesion volume divided by the number of lesions 4) total largest diameter, defined as the largest diameter spanning all lesions 5) sum of the largest diameters, defined as the sum of the largest diameters of individual lesions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eIn the data pre-processing step, a variable was created for the relative difference in each of the five MRI tumor load features, by dividing the end-of-treatment feature value by the pre-treatment feature value (hereafter: change in MRI tumor load). The MRI variables and %TILs values were transformed into variables with normal-shaped distributions using a Box-Cox procedure. To be able to use cases with incomplete data in the model development and evaluation, we imputed missing values. For cohort A and B, if the end-of-treatment tumor load features were missing and they were available at an earlier point (from a scan during treatment), the earlier time-point feature values were used for imputation by last observation carried forward. If the features were only available at one timepoint, the sample mean was used for the change in tumor load features. The %TILs and RCB were imputed with the sample mean.\\u003c/p\\u003e \\u003cp\\u003eDifferent prediction models were developed to explore the possible additional value of predictors: a model with only %TILs, a model with only change in MRI tumor load, and a third model combining change in MRI tumor load and %TILs. The individual %TILs and change in MRI tumor load models were developed and evaluated in the whole patient population, and then evaluated in the ER+/HER2- and triple negative (TN)/HER2\\u0026thinsp;+\\u0026thinsp;subgroups separately. Each model was a (main effects) linear regression model with RCB as the dependent variable, fit using L1-penalised maximum likelihood estimation (LASSO) with the penalty parameter set at the value that yielded the lowest mean squared error in an inner-loop 10-fold cross validation scheme. To estimate the expected out-of-sample performance of the various models in terms of discrimination, we used an additional outer-loop cross validation (CV) with 5 folds and 10 repeats. Discrimination for pCR (RCB 0) vs. residual disease ( RCB\\u0026thinsp;\\u0026gt;\\u0026thinsp;0) was evaluated using receiver operating characteristic (ROC) curves and, in particular, the area under the ROC curve (AUC). 95% confidence intervals (CI) were estimated by bootstrapping the original data and performing repeated cross-validation in each bootstrap sample.\\u003c/p\\u003e \\u003cp\\u003eFor estimation of the median follow-up time after NAC, the reverse Kaplan Meier method was used. For exploring the association between %TILs and recurrence-free survival (RFS, as previously defined(\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e)), we used Cox regression analysis with the box-cox transformed %TILs as the explaining variable, calculating hazard ratios (HR). In order to create Kaplan Meier curves, the original %TILs values were stratified in two predefined groups of 1\\u0026ndash;10% and \\u0026gt;\\u0026thinsp;10% TILs, corresponding to the low vs. intermediate\\u0026thinsp;+\\u0026thinsp;high groups of a large pooled analysis(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). We merged the intermediate and high subgroups of TILs because of the expected lower TILs counts in the ER+/HER2- group. All statistical analysis were performed in R software version 4.2.2.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eA total of 190 patients were included in this study (129 in cohort A, 61 in B) of which 106 were ER+/HER2-, 40 ER-/HER2- and 44 ER+-/HER2+. All patients underwent surgery after NAC, 51 patients reached pCR (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Supplementary Tables\\u0026nbsp;1 and 2). Median follow up time after NAC was 58 months. There were a total of 31 RFS events.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePatient and tumor characteristics of breast cancer patients treated with NAC that were included in the study, overall and according to pCR vs. residual disease, %TILs and cohort. cT stage: clinical tumor stage according to the American Joint Committee on Cancer (AJCC) staging system. pCR: pathological complete response\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;190)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e%TILs 1\\u0026ndash;10*\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;122)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e%TILs\\u0026thinsp;\\u0026gt;\\u0026thinsp;10*\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;68)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCohort A\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;129)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eCohort B\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;61)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMedian [Min, Max]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50 [25\\u0026ndash;78]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52.50 [25.00, 78.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48.50 [25.00, 71.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e50.00 [25.00, 78.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e50.00 [25.00, 72.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHistology (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInvasive carcinoma NST\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e132 (69.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e79 (64.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 (77.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e79 (61.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e53 (86.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDuctolobular carcinoma\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33 (17.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26 (21.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7 (10.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e31 (24.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2 ( 3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLobular carcinoma\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (10.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15 (12.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 ( 7.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e15 (11.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5 ( 8.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOther\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 ( 2.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 ( 4.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4 ( 3.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGrade (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17 (8.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14 (11.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 ( 4.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e17 (13.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92 (48.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e64 (52.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28 (41.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e68 (52.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e24 (39.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (42.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e44 (36.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e37 (54.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e44 (34.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e37 (60.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMissing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eIHC subtype (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eER-/HER2-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40 (21.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22 (18.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e18 (26.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e19 (14.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e21 (34.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eER+-/HER2+\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44 (23.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25 (20.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19 (27.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e27 (20.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e17 (27.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eER+/HER2-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e106 (55.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e75 (61.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31 (45.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e83 (64.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e23 (37.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ecT stage (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30 (15.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19 (15.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11 (16.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e19 (14.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e11 (18.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e111 (58.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e75 (62.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e36 (52.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e74 (57.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e37 (61.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (19.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21 (17.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16 (23.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e25 (19.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e12 (20.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT4 a-b\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (5.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 ( 5.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 ( 7.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11 ( 8.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMissing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNodal metastases (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90 (47.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60 (49.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30 (44.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e67 (51.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e23 (37.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e100 (52.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62 (50.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38 (55.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e62 (48.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e38 (62.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e%TILs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e122 (64.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e76 (58.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e46 (75.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68 (35.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e53 (41.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e15 (24.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNeoadjuvant treatment (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFEC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 ( 1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFEC-DOC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79 (41.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53 (43.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26 (38.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e79 (61.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDOC\\u0026thinsp;+\\u0026thinsp;CP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 ( 0.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 ( 2.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3 ( 2.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTAC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 ( 0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 ( 1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1 ( 0.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAC-P\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42 (22.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28 (23.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14 (20.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e16 (12.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e26 (42.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAC-P\\u0026thinsp;+\\u0026thinsp;trastuzumab\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (12.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11 ( 9.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13 (19.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e24 (18.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAC-P\\u0026thinsp;+\\u0026thinsp;carboplatin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18 ( 9.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12 ( 9.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6 ( 8.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e18 (29.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePaclitaxel\\u0026thinsp;+\\u0026thinsp;trastuzumab\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 ( 1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1 ( 0.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePTCP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18 ( 9.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12 ( 9.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6 ( 8.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2 ( 1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e16 (26.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAC\\u0026thinsp;+\\u0026thinsp;2x intensified CP, thiotepa and carboplatin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 ( 0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 ( 0.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1 ( 0.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0 ( 0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRelative change on MRI*\\u003c/b\\u003e median [min, max]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber of lesions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63.7 [0.0, 400.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e66. 7 [0.0, 400.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e50.0 [0.0, 250.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e66.7 [0.0, 200.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e50.0 [0.0, 400.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal volume\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.5 [0.0, 221.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.7 [0.0, 115.6]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.6 [0.0, 221.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.0 [0.0, 221.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.8 [0.0, 63.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean volume\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.5 [0.0, 221.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.0 [0.0, 97.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.6 [0.0, 221.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.5 [0.0, 221.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.6 [0.0, 71.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal largest diameter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44.2 [0.0, 425.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50.6 [0.0, 425.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.5 [0.0, 289.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e426 [0.0, 425.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e47.1 [0.0, 328.1]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSum largest diameter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34.9 [0.0, 192.9]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e41.6 [0.0, 192.9]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.9 [0.0, 135.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e41.3 [0.0, 192.9]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e31.9 [0.0, 135.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSurgery type (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLumpectomy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e99 (52.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e66 (54.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33 (48.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e74 (57.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e25 (41.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMastectomy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e91 (47.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56 (45.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35 (51.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e55 (42.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e36 (59.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRCB *\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMedian [min, max]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.54 [0.00, 4.22]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.61 [0.00, 3.72]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.23 [0.00, 4.22]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.55 [0.00, 4.22]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.22 [0.00, 3.42]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eFEC: 5-fluorouracil, epirubicin, cyclophosphamide, DOC: docetaxel, CP: cyclophosphamide, AC-P: Doxorubicin, cyclophosphamide and paclitaxel. PTCP: Paclitaxel, trastuzumab, carboplatin and pertuzumab, TAC: Docetaxel, doxorubicin and cyclophosphamide, AC: Doxorubicin, cyclophosphamide.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e* Values after imputation\\u003c/h2\\u003e \\u003cp\\u003eThere were a total of 13% missing values for the change in tumor load MRI variables and 15% for the %TILs values. RCB was missing in 2 cases. See Supplementary Tables\\u0026nbsp;1 and 2 for missing data per cohort. Proportion of missing data was comparable among the IHC subtypes.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssessment of response to NAC\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e depicts the ROC curves and corresponding (cross validated) AUC of each of the models. Coefficients can be found in Supplementary Table\\u0026nbsp;3. A prediction model containing the change in MRI tumor load reached an estimated CV AUC of 0.69 (95% CI 0.61\\u0026ndash;0.79) in all patients, and a model with only %TILs had an estimated CV AUC of 0.69 (95% CI 0.53\\u0026ndash;0.78) A prediction model combining %TILs and change in MRI tumor load had an estimated CV AUC of 0.75(95% CI 0.67\\u0026ndash;0.83). The change in MRI tumor load model evaluated in the ER+/HER2- patient group yielded an estimated CV AUC of 0.67(95% CI 0.51\\u0026ndash;0.84), the %TILs-only model an estimated CV AUC of 0.68 (95% CI 0.50\\u0026ndash;0.82), while the combined model had an estimated CV AUC of 0.72 (95%CI 0.60\\u0026ndash;0.88). For the TN\\u0026amp;HER2\\u0026thinsp;+\\u0026thinsp;subgroup, the change in MRI tumor load model had an estimated CV AUC of 0.67 (95% CI 0.56\\u0026ndash;0.79), the %TILs only model an estimated CV AUC of 0.63 (95% CI 0.49\\u0026ndash;0.74), while the combined model had an estimated CV AUC of 0.70(95% CI 0.59\\u0026ndash;0.82).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eExplorative analysis of TILs vs. event-free survival\\u003c/h2\\u003e \\u003cp\\u003e%TILs was significantly associated with RFS in all patients (HR 0.72(95% CI 0.53\\u0026ndash;0.98), for every standard deviation increase in %TILS, p\\u0026thinsp;=\\u0026thinsp;0.038). This association does not appear substantially different in the two subgroups (HR 0.68 (95% CI 0.44\\u0026ndash;1.074), p\\u0026thinsp;=\\u0026thinsp;0.10 in the ER+/HER2- group and HR 0.72 (95% CI 0.44\\u0026ndash;1.19), p\\u0026thinsp;=\\u0026thinsp;0.20 in the TN\\u0026amp;HER2\\u0026thinsp;+\\u0026thinsp;group). Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the survival curves of patients with %TILs 1\\u0026ndash;10 vs. \\u0026gt;10 in each of the groups. %TILs in the resection specimen was also evaluated but was not available in all patients resulting in insufficient data to properly assess the association with RFS.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this multicenter study, we explored the combination of %TILs and change in tumor load on DCE-MRI to assess response to NAC in patients with ER+/HER2- and TN\\u0026amp;HER2\\u0026thinsp;+\\u0026thinsp;breast cancer. A higher CV AUC was observed for the combination of %TILs and change in tumor load on MRI compared to either one alone in the whole group ((0.75 (95% CI 0.67\\u0026ndash;0.83) vs. 0.69 for %TILS-only (95% CI 0.53\\u0026ndash;0.78) and 0.69 (95%CI 0.61\\u0026ndash;0.79) for change in MRI tumor load only). This was also observed in the ER+/HER2- group ((0.72 (95%CI 0.60\\u0026ndash;0.88) vs. 0.68 (95% CI 0.50\\u0026ndash;0.82) and vs. 0.67 (95% CI 0.51\\u0026ndash;0.82), as well as in the TN\\u0026amp;HER2\\u0026thinsp;+\\u0026thinsp;group ((0.70 (95% CI 0.59\\u0026ndash;0.82) vs. 0.63 (95% CI 0.49\\u0026ndash;0.74) and vs. 0.67 (95% CI 0.56\\u0026ndash;0.79).\\u003c/p\\u003e \\u003cp\\u003eThe difference in observed discriminative ability should, however, be interpreted with caution given the wide confidence intervals.\\u003c/p\\u003e \\u003cp\\u003eThere is a large need for improvement of response prediction, before clinical trials on postponing or omitting surgery after NAC have a good chance of succeeding (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Our work suggests that %TILs and MRI may hold complementary information and could be a useful combined biomarker for response to NAC in different breast cancer subtypes.\\u003c/p\\u003e \\u003cp\\u003eTILs have been shown by others to be correlated to pCR in the HER2\\u0026thinsp;+\\u0026thinsp;and TN subtypes, with higher TILs relating to higher pCR rates (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). In the ER+/HER2- subtype, the literature is inconclusive. A large pooled analysis by Denkert et al. showed a significant positive correlation between TILs and pCR in the ER+/HER2- subtype(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). A different meta-analysis and some other smaller studies did, however, not find this correlation (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). TILs are reported to be less frequent in ER+/HER2- breast cancer compared to the other subtypes (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e), which makes it less likely to find a correlation in smaller groups. We found an association between TILs and response to NAC as measured by RCB in the whole group of patients. One study found significant correlations between RCB classes and TIL CD8/FOXP3 ratio in TN breast cancer (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). A different study by Elmahs et al. did not find a correlation between TILs and RCB class, perhaps due to small sample size (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eImmunotherapy is a topic of increasing interest in breast cancer treatment, initially investigated in the metastatic setting, followed by the neoadjuvant setting. In the TN subtype, the combination of immunotherapy and NAC was shown to improve pCR in 2020(\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). In TN patients treated with this combination, higher sTILs were associated with higher pCR rates (\\u003cspan additionalcitationids=\\\"CR32\\\" citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). More recently, promising results for the combination of immunotherapy and NAC have been proposed in the luminal subtype as well (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e). Personalizing treatment and not giving more than necessary is of special importance in the light of high costs associated with immunotherapy. In a recent study by Loi et. al., presented at SABCS 2023, higher pCR rates were seen with increasing sTILs in patients with high risk luminal breast cancer treated with NAC and nivolumab(\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). Models like the one presented in this study could potentially aid clinical decision making in treatment with the combination of NAC and immunotherapy in the future. They should however be evaluated in a population treated with this regimen.\\u003c/p\\u003e \\u003cp\\u003eWith regard to the prognostic value of TILs, we found that higher %TILs in biopsy is associated with better RFS after NAC in the whole cohort. This suggests that %TILs could also be useful for post NAC decision making, although its role in relation to other prognostic factors was not investigated here due to too few events. High TILs have been reported to be correlated to better prognosis in the TN and HER2\\u0026thinsp;+\\u0026thinsp;subtypes (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e). In the ER+/HER2- group, the pooled analysis by Denkert et. al. reported low TILs (0\\u0026ndash;10%) to be correlated with improved disease free survival, in contrast with our results(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). The meta-analysis by Li et. al. reported no correlation between TILs and survival(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Our work thus contributes to the growing body of research on the prognostic role of TILs in breast cancer. We did not have enough data to evaluate the relationship with TILs in the residual tumor to RFS, but work is underway to incorporate TILs in the residual tumor after NAC in the RCB to further stratify post-neoadjuvant prognosis(\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e).Since MRI is more accurate in evaluating response to NAC compared to mammography, ultrasound and physical examination, it is widely used in clinical practice (\\u003cspan additionalcitationids=\\\"CR40\\\" citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). Radiological assessment alone is, however, not accurate enough to guide treatment decisions (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). A (semi-) automated method for evaluating response to NAC could be of interest, since manual measurement by RECIST is associated with intra- and interobserver variability (\\u003cspan additionalcitationids=\\\"CR44\\\" citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTissue biopsy is always a part of the diagnostic pre-treatment work-up and assessing TILs in the biopsy is quick and easily implemented, possibly even more so when artificial intelligence algorithms are deployed (\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e). The combination of TILs and computer extracted MRI features may therefore be an efficient use of information that is available from the clinical workflow without additional (invasive) procedures. Our results suggest the complementary value of these different data sources in assessing response to NAC, which could ultimately help in sparing patients unnecessary treatment.\\u003c/p\\u003e \\u003cp\\u003eOur study has several limitations. First, there was no independent cohort to perform external validation of the developed models. Second, due to limited sample size, we were unable to account for relevant predictors such as treatment regimen and nodal status, or to evaluate the HER2\\u0026thinsp;+\\u0026thinsp;and TN subtypes separately. Third, our two cohorts contain patients from different periods in time, which resulted in different treatment regimens that may not reflect current clinical practice. Additionally, for cohort A, not all biopsies were centrally revised for ER, HER2 and nodal status. This could result in unwanted interobserver variability in these variables, which is however a reflection of clinical practice. Lastly, the MRI processing differed between cohort A and B. In theory, this could have impacted the results, although the methods have been shown to lead to highly correlated results(\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, our results show that the combination of TILs and change in tumor load on MRI is informative of response after NAC overall, as well as in the ER+/HER2- and TN\\u0026amp;HER2\\u0026thinsp;+\\u0026thinsp;groups separately. This could be of interest for clinical trials on de-escalating surgical intervention. More work is, however, needed to reduce uncertainty and improve accuracy by modifying for other predictors as well.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting Interests\\u003c/h2\\u003e \\u003cp\\u003eAll authors have declared no conflict of interest.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eEthics approval\\u003c/h2\\u003e \\u003cp\\u003e Cohort A: Requirement for informed consent and ethical review was waived by the institutional review board (Medical Research Ethics Committee Utrecht, no. 19\\u0026ndash;245).\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis project was funded in part by the European Union Horizon 2020 research and innovation program [grant number 755333]\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eL.M. Janssen, S. G. Elias, E. van der Wall, P. J. van Diest and K. G. A. Gilhuijs contributed to the study design. L.M. Janssen collected and curated the clinical data and wrote the manuscript. L.M. Janssen and B. B. L. Penning de Vries conducted the statistical analysis. S. G. Elias and B. B. L. Penning de Vries provided biostatistical and epidemiological support. M. H. A. Janse provided quantitative MRI data. P. J. van Diest performed the pathology revision. R. Salgado performed the TILs assessment. All authors revised the manuscript and approved the final version.\\u003c/p\\u003e\\u003ch2\\u003eData availability\\u003c/h2\\u003e \\u003cp\\u003eDatasets and R code used for analysis are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSymmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V et al (2007) Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 25(28):4414\\u0026ndash;4422\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N et al (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet (London England) 384(9938):164\\u0026ndash;172\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYau C, Osdoit M, van der Noordaa M, Shad S, Wei J, de Croze D et al (2021) Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients. Lancet Oncol\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHassett MJ, O'Malley AJ, Pakes JR, Newhouse JP, Earle CC (2006) Frequency and Cost of Chemotherapy-Related Serious Adverse Effects in a Population Sample of Women With Breast Cancer. 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Breast Cancer Res Treat 132(3):793\\u0026ndash;805\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHwang HW, Jung H, Hyeon J, Park YH, Ahn JS, Im YH et al (2019) A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Breast Cancer Res Treat 173(2):255\\u0026ndash;266\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRusso L, Maltese A, Betancourt L, Romero G, Cialoni D, De la Fuente L et al (2019) Locally advanced breast cancer: Tumor-infiltrating lymphocytes as a predictive factor of response to neoadjuvant chemotherapy. Eur J Surg Oncol 45(6):963\\u0026ndash;968\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLoi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F et al (2013) Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02\\u0026ndash;98. J Clin Oncol 31(7):860\\u0026ndash;867\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiyashita M, Sasano H, Tamaki K, Chan M, Hirakawa H, Suzuki A et al (2014) Tumor-infiltrating CD8\\u0026thinsp;+\\u0026thinsp;and FOXP3\\u0026thinsp;+\\u0026thinsp;lymphocytes in triple-negative breast cancer: its correlation with pathological complete response to neoadjuvant chemotherapy. Breast Cancer Res Treat 148(3):525\\u0026ndash;534\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eElmahs A, Mohamed G, Salem M, Omar D, Helal AM, Soliman N (2022) The Impact of Tumor Infiltrating Lymphocytes Densities and Ki67 Index on Residual Breast Cancer Burden following Neoadjuvant Chemotherapy. Int J Breast Cancer 2022:2597889\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSchmid P, Cortes J, Pusztai L, McArthur H, K\\u0026uuml;mmel S, Bergh J et al (2020) Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med 382(9):810\\u0026ndash;821\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWood SJ, Gao Y, Lee JH, Chen J, Wang Q, Meisel JL, Li X (2024) High tumor infiltrating lymphocytes are significantly associated with pathological complete response in triple negative breast cancer treated with neoadjuvant KEYNOTE-522 chemoimmunotherapy. Breast Cancer Res Treat\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLoibl S, Untch M, Burchardi N, Huober J, Sinn BV, Blohmer JU et al (2019) A randomised phase II study investigating durvalumab in addition to an anthracycline taxane-based neoadjuvant therapy in early triple-negative breast cancer: clinical results and biomarker analysis of GeparNuevo study. Ann Oncol 30(8):1279\\u0026ndash;1288\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSchmid P, Salgado R, Park YH, Mu\\u0026ntilde;oz-Couselo E, Kim SB, Sohn J et al (2020) Pembrolizumab plus chemotherapy as neoadjuvant treatment of high-risk, early-stage triple-negative breast cancer: results from the phase 1b open-label, multicohort KEYNOTE-173 study. Ann Oncol 31(5):569\\u0026ndash;581\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLoi S, Curigliano G, Salgado RF, Romero Diaz RI, Delaloge S, Rojas C et al (2023) LBA20 A randomized, double-blind trial of nivolumab (NIVO) vs placebo (PBO) with neoadjuvant chemotherapy (NACT) followed by adjuvant endocrine therapy (ET)\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;NIVO in patients (pts) with high-risk, ER\\u0026thinsp;+\\u0026thinsp;HER2\\u0026thinsp;\\u0026ndash;\\u0026thinsp;primary breast cancer (BC). Ann Oncol 34:S1259\\u0026ndash;S60\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCardoso F, McArthur HL, Schmid P, Cort\\u0026eacute;s J, Harbeck N, Telli ML et al (2023) LBA21 KEYNOTE-756: Phase III study of neoadjuvant pembrolizumab (pembro) or placebo (pbo)\\u0026thinsp;+\\u0026thinsp;chemotherapy (chemo), followed by adjuvant pembro or pbo\\u0026thinsp;+\\u0026thinsp;endocrine therapy (ET) for early-stage high-risk ER+/HER2\\u0026ndash; breast cancer. Ann Oncol 34:S1260\\u0026ndash;S1\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSherene Loi GC, Roberto Salgado Roberto Iv\\u0026aacute;n Romero D\\u0026iacute;az, Suzette Delaloge CIRG, Marleen Kok, Cristina Saura, Nadia Harbeck EAM, Denise A. Yardley, Lajos Pusztai, Alberto Su\\u0026aacute;rez Zaizar AU, Felipe Ades, Rajalakshmi Chandra, Raheel Nathani MP, Thomas Spires,16 Jenny Qun Wu, Heather McArthur. Biomarker results in high-risk estrogen receptor\\u0026ndash;positive, human epidermal growth factor receptor 2\\u0026ndash;negative primary breast cancer following neoadjuvant chemotherapy\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;nivolumab: an exploratory analysis of CheckMate 7FL. San Antonio Breast Cancer Symposium; December 5\\u0026ndash;9 20232023\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMart\\u0026iacute;n M, Yoder R, Salgado R, Del Monte-Mill\\u0026aacute;n M, Alvarez EL, Echavarr\\u0026iacute;a I et al (2024) Tumor-infiltrating lymphocytes refine outcomes in triple-negative breast cancer treated with anthracycline-free neoadjuvant chemotherapy. Clin Cancer Res\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDieci MV, Radosevic-Robin N, Fineberg S, van den Eynden G, Ternes N, Penault-Llorca F et al (2018) Update on tumor-infiltrating lymphocytes (TILs) in breast cancer, including recommendations to assess TILs in residual disease after neoadjuvant therapy and in carcinoma in situ: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer. Sem Cancer Biol 52:16\\u0026ndash;25\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eScheel JR, Kim E, Partridge SC, Lehman CD, Rosen MA, Bernreuter WK et al (2018) MRI, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial. AJR American journal of roentgenology. ;210(6):1376-85\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMarinovich ML, Macaskill P, Irwig L, Sardanelli F, Mamounas E, von Minckwitz G et al (2015) Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer 15:662\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePark J, Chae EY, Cha JH, Shin HJ, Choi WJ, Choi YW, Kim HH (2018) Comparison of mammography, digital breast tomosynthesis, automated breast ultrasound, magnetic resonance imaging in evaluation of residual tumor after neoadjuvant chemotherapy. Eur J Radiol 108:261\\u0026ndash;268\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJanssen LM, den Dekker BM, Gilhuijs KGA, van Diest PJ, van der Wall E, Elias SG (2022) MRI to assess response after neoadjuvant chemotherapy in breast cancer subtypes: a systematic review and meta-analysis. NPJ breast cancer 8(1):107\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBeresford MJ, Padhani AR, Taylor NJ, Ah-See ML, Stirling JJ, Makris A et al (2006) Inter- and intraobserver variability in the evaluation of dynamic breast cancer MRI. J Magn Reson Imaging 24(6):1316\\u0026ndash;1325\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSuzuki C, Torkzad MR, Jacobsson H, \\u0026Aring;str\\u0026ouml;m G, Sundin A, Hatschek T et al (2010) Interobserver and intraobserver variability in the response evaluation of cancer therapy according to RECIST and WHO-criteria. Acta Oncol 49(4):509\\u0026ndash;514\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKarmakar A, Kumtakar A, Sehgal H, Kumar S, Kalyanpur A (2019) Interobserver Variation in Response Evaluation Criteria in Solid Tumors 1.1. Acad Radiol 26(4):489\\u0026ndash;501\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlbusayli R, Graham JD, Pathmanathan N, Shaban M, Raza SEA, Minhas F et al (2023) Artificial intelligence-based digital scores of stromal tumour-infiltrating lymphocytes and tumour-associated stroma predict disease-specific survival in triple-negative breast cancer. J Pathol 260(1):32\\u0026ndash;42\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"breast-cancer-research-and-treatment\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"brea\",\"sideBox\":\"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)\",\"snPcode\":\"10549\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10549/3\",\"title\":\"Breast Cancer Research and Treatment\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Breast cancer, tumor infiltrating tumor cells, magnetic resonance imaging, neoadjuvant chemotherapy, pathological complete response\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4114099/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4114099/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e \\u003cp\\u003eIn this study, we aimed to explore if the combination of tumor infiltrating lymphocytes (TILs) and change in tumor load on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) leads to better assessment of response to neoadjuvant chemotherapy (NAC) in patients with breast cancer, compared to either alone.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eIn 190 NAC treated patients, MRI scans were performed before and at the end of treatment. The percentage of stromal TILs (%TILs) was assessed in pre-NAC biopsies according to established criteria. Prediction models were developed with linear regression by least absolute shrinkage and selection operator (LASSO) and cross validation (CV), with residual cancer burden (RCB) as the dependent variable. Discrimination for pathological complete response (pCR) was evaluated using area under the receiver operating characteristic curves (AUC). We used Cox regression analysis for exploring the association between %TILs and recurrence-free survival (RFS).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eFifty-one patients reached pCR. In all patients, the %TILs model and change in MRI tumor load model had an estimated CV AUC of 0.69 (95% confidence interval (CI) 0.53\\u0026ndash;0.78) and 0.69 (95%CI 0.61\\u0026ndash;0.79), respectively, whereas a model combining the variables resulted in an estimated CV AUC of 0.75 (95% CI 0.66\\u0026ndash;0.83). In the group with tumors that were ER positive and HER2 negative (ER+/HER2-) and in the group with tumors that were either triple negative or HER2 positive (TN\\u0026amp;HER2+) separately, the combined model reached an estimated CV AUC of 0.72 (95%CI 0.60\\u0026ndash;0.88) and 0.70(95%CI 0.59\\u0026ndash;0.82), respectively. A significant association was observed between pre-treatment %TILS and RFS (hazard ratio (HR) 0.72 (95% CI 0.53\\u0026ndash;0.98), for every standard deviation increase in %TILS, p\\u0026thinsp;=\\u0026thinsp;0.038).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThe combination of TILs and MRI is informative of response to NAC in patients with both ER+/HER2- and TN\\u0026amp;HER2\\u0026thinsp;+\\u0026thinsp;tumors.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-21 18:10:06\",\"doi\":\"10.21203/rs.3.rs-4114099/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-04-15T11:24:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-04-09T22:31:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-04-04T09:41:33+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"2d820fb5-7c6e-455e-85b5-9a08b80afde4\",\"date\":\"2024-03-27T13:47:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"8cc7f7fb-4dbd-4b27-a83d-2bfd5ae1f419\",\"date\":\"2024-03-26T02:44:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-03-26T02:35:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-03-18T11:27:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-03-18T11:27:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Breast Cancer Research and Treatment\",\"date\":\"2024-03-16T16:48:56+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"breast-cancer-research-and-treatment\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"brea\",\"sideBox\":\"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)\",\"snPcode\":\"10549\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10549/3\",\"title\":\"Breast Cancer Research and Treatment\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"fd1900f6-6614-49b8-8dd0-24158b75dfcf\",\"owner\":[],\"postedDate\":\"March 21st, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-09-23T17:48:54+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4114099\",\"link\":\"https://doi.org/10.1007/s10549-024-07484-7\",\"journal\":{\"identity\":\"breast-cancer-research-and-treatment\",\"isVorOnly\":false,\"title\":\"Breast Cancer Research and Treatment\"},\"publishedOn\":\"2024-09-16 15:57:46\",\"publishedOnDateReadable\":\"September 16th, 2024\"},\"versionCreatedAt\":\"2024-03-21 18:10:06\",\"video\":\"\",\"vorDoi\":\"10.1007/s10549-024-07484-7\",\"vorDoiUrl\":\"https://doi.org/10.1007/s10549-024-07484-7\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4114099\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4114099\",\"identity\":\"rs-4114099\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}