Usefulness of interim contrast-enhanced breast MRI for predicting response of breast cancer to neoadjuvant chemotherapy

Usefulness of interim contrast-enhanced breast MRI for predicting response of breast cancer to neoadjuvant chemotherapy | 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 Usefulness of interim contrast-enhanced breast MRI for predicting response of breast cancer to neoadjuvant chemotherapy Eri Kato, Shinsuke Sasada, Norio Masumoto, Akiko Emi, Hideo Shigematsu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4230661/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose In breast cancer patients, contrast-enhanced breast magnetic resonance imaging (MRI) is usually performed after completing neoadjuvant chemotherapy (NAC) to determine treatment efficacy. We compared the ability of MRI conducted during the interim phase (interim MRI) with that of MRI conducted after NAC (post-NAC MRI) to predict treatment response. Methods In this retrospective study, 260 patients with invasive breast cancer who underwent NAC between April 2010 and December 2020 and who had undergone MRI before, during, and after NAC were included. Most patients received taxane and anthracycline sequentially, while human epidermal growth factor receptor 2 (HER2)-positive cases also received anti-HER2 agents. Results Sixty-five patients (25%) demonstrated a pathological complete response (pCR). The sensitivity and specificity of interim MRI for predicting pCR in all patients were 84% and 83%, respectively, which were comparable with those of post-NAC MRI (88% and 64%, respectively). Its sensitivity and specificity in luminal-type breast cancer were 95% and 50%, respectively, and those in HER2-positive breast cancer were 77% and 73%, respectively, while those in triple-negative breast cancer (TNBC) cases were 94% and 89%, respectively. Twenty-nine (83%) of 35 cases diagnosed with clinical complete response (cCR) by interim MRI achieved pCR, whereas 25 (93%) of the 27 HER2-positive type or TNBC cases achieved pCR. Similarly, 189 (84%) of 225 cases indicated as non-cCR by interim MRI were non-pCR, whereas 94 (95%) of 99 non-cCR luminal-type cases were non-pCR. Conclusion Interim MRI could predict treatment response at an early stage in breast cancer patients receiving NAC. breast cancer dynamic contrast-enhanced magnetic resonance imaging neoadjuvant chemotherapy pathological response sensitivity specificity Figures Figure 1 Figure 2 Figure 3 Introduction Neoadjuvant chemotherapy (NAC) is one of the standard treatments for breast cancer. It reduces the extent of surgical resection required and can indicate the sensitivity of cancer to anti-cancer drugs [ 1 ]. In human epidermal growth factor receptor 2 (HER2)-positive breast cancer and triple-negative breast cancer (TNBC), NAC typically achieves a pathological complete response (pCR) rate as high as 50–80%. As patients who achieve pCR by NAC have a good prognosis, NAC is particularly important in HER2-positive and TNBC cases. Hence, knowing the anti-cancer drug sensitivity is important to optimize the treatment plan and improve patient outcomes [ 2 ]. The main imaging modalities used to determine the effectiveness of NAC are ultrasonography, positron emission tomography–computed tomography (PET-CT), and contrast-enhanced breast magnetic resonance imaging (MRI) [ 3 , 4 ]. Among these modalities, contrast-enhanced MRI can accurately delineate the extent of breast cancer and is useful in determining the extent of surgical resection required [ 5 – 7 ]. However, the appropriate timing of such imaging for determining response at an early stage remains inconclusive. Such imaging might be conducted after two cycles of chemotherapy when using PET-CT or after one or two courses of chemotherapy when using contrast-enhanced breast MRI [ 6 , 7 ]. The timing of efficacy determination and the imaging modalities used in cases undergoing NAC are crucial as omitting surgery can be considered in cases where pCR is predicted [ 8 – 12 ]. Currently, imaging is conducted only once NAC is completed [ 13 ]. However, early efficacy testing can result in improved pCR rates as it would allow changes in the NAC regimen in cases showing poor response to the initial NAC used [ 6 ]. We considered that contrast-enhanced breast MRI in patients undergoing NAC conducted during the interim period might predict treatment response at an early stage. Here, we reported a detailed retrospective analysis of the efficacy of interim MRI to reflect response to treatment, including that in distinct clinical subtypes of breast cancer. Patients and Methods Patients A total of 260 consecutive patients with invasive breast cancer who underwent NAC at Hiroshima University Hospital between April 2010 and December 2020 and who had undergone breast MRI before, during, and after completion of NAC were included in this study. Patients essentially received four courses each of taxane (4 cycles docetaxel 75 mg/m 2 ) and anthracycline (4 cycles epirubicin 90 mg/m 2 , cyclophosphamide 600 mg/m 2 ) chemotherapy combined with anti-HER2 therapy in cases of HER2-positive breast cancer. We excluded patients aged below 18 years, those who were not prescribed NAC, and those who did not undergo contrast-enhanced breast MRI. The study followed the Helsinki Declaration guidelines and was approved by the Institutional Review Board of Hiroshima University Hospital (approval number E2014-1157-06). Patients provided informed consent to participate in this study. Pathological diagnosis In this study, pCR was defined as the absence of invasive disease or intraepithelial ductal carcinoma in the breast and lymph nodes (ypT0ypN0). HER2 positivity was defined as HER2 3 + or HER2 2 + and fluorescent in situ hybridization-positive staining [ 14 ]. Hormone receptor (HR)-positive cases were considered luminal breast cancers. Cases with positive estrogen receptor (ER) and progesterone receptor (PgR) staining of ≥ 1% were considered ER or PgR positive, respectively. HR and HER2 status were assessed according to the guidelines of the American Society of Clinical Oncology/College of American Pathologists [ 15 – 17 ]. The molecular subtypes of breast cancer were classified as luminal (ER+/ HER2-), HER2-type (ER+/HER2+, ER-/HER2+), or TNBC (ER-/HER2-). Contrast-enhanced breast MRI All patients underwent contrast-enhanced breast MRI with a 1.5-T 7-channel table plane coil (Philips Achieva and Philips Ingenia, respectively; Philips Healthcare, Best, The Netherlands) before, during (after four courses of NAC), and after completion of NAC. Images were evaluated in a multidisciplinary meeting that included a breast surgeon, pathologist, radiologist, and ultrasound examination technician. For axial plane images, the contrast media used were 0.1 mmol/kg dimeglumine gadopentetate (Magnevist; Bayer, Osaka, Japan) or gadobutrol (Gadovist; Bayer). In addition, a 3D gradient echo sequence (repetition time/echo time, 3.9/1.9 ms; flip angle, 10°; field-of-view: diffusion-weighted imaging, 2.73 mm/3.46 mm/5.0 mm; T1-weighted imaging, 1.00 mm/1.14 mm/5.0 mm; T2-weighted fat-suppressed, 0.95 mm/1.33 mm/5.0 mm, dynamic, 1.05 mm/1.17 mm/1.6 mm; T1-weighted fat-suppressed sagittal, 0.9 mm/0.9 mm/2.0 mm) was used to acquire contrast-enhanced unilateral sagittal images, with high spatial resolution, of the cancer-affected and contralateral breasts. The images were acquired at 70, 140, 180, and 320 s after contrast injection. Complete response on contrast-enhanced breast MRI was defined as the absence of contrast enhancement in the early and late nonsubtracted fat-suppressed dynamic contrast-enhanced axial images or a faint enhancement that was less than or equal to that of normal mammary glands (Fig. 1 ). Clinical complete response (cCR) was evaluated on interim and post-NAC MRI. Statistical analysis For this study, high sensitivity was defined as the ability to diagnose residual lesions. The software program JMP ® Pro 16 (SAS Institute, Cary, NC, USA) was used for statistical analyses to calculate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and test accuracy. Univariate and multivariate analyses were performed using logistic regression analysis. Area under the curve (AUC) was used to assess the discriminative power of the model. A significance level of p values < 0.05 was used. Results The characteristics of the 260 participants included in this study are shown in Table 1 . Patients’ mean age was 52 ± 11 years. Most patients had stage II cancer (Table 1 ). In terms of clinical subtypes, luminal type accounted for the most cases, followed by the HER2 type and then TNBC. pCR was achieved by NAC in one-quarter of patients, predominantly in those with the HER2 type and TNBC. Table 1 Characteristics of patients included in this study Characteristic N = 260 (%) Age, mean ± SD 52 ± 11 years Median (range) 51 (22–72) years Tumor stage Ⅰ 28 (11) Ⅱ 166 (64) Ⅲ 66 (25) Clinical T T1 53 (20) T2 153 (59) T3 26 (10) T4 28 (11) Clinical N 1 135 (52) 2 98 (38) 3 27 (10) Nuclear grade 1 15 (6) 2 73 (28) 3 167 (64) Ki-67 labeling index <20% 28 (11) ≥20% 210 (81) Subtype Luminal 107 (41) HER2 type 83 (32) TNBC 70 (27) Pathological response pCR 65 (25) Luminal 9 (13) HER2 type 32 (49) TNBC 24 (37) Non-pCR 195 (75) Luminal 98 (50) HER2 type 51 (26) TNBC 46 (24) Abbreviations: NAC, neoadjuvant chemotherapy; pCR, pathologic complete response; SD, standard deviation; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer The regimens of NAC used for the 260 patients are shown in Fig. 1 . These included taxane to 5-fluorouracil, epidoxorubicin, and cyclophosphamide (FEC) in 96 patients, FEC to taxane in 49 patients, taxane + anti-HER2 drug to FEC in 59 patients, FEC to taxane + anti-HER2 drug in 7 patients, and other regimens in 49 patients. Most patients with HER2-positive breast cancer had received prior taxane + anti-HER2 drugs. The accuracy of interim and post-NAC MRI in predicting pCR were similar and exceeded 80% in both cases. The sensitivity, specificity, PPV, NPV, and accuracy of both approaches are shown in Table 2 . The AUC was 0.81 for interim MRI and 0.82 for post-NAC MRI. Table 2 Accuracy of interim MRI and post-NAC MRI Interim MRI (95% CI) Post-NAC MRI (95% CI) Sensitivity 0.84 (0.77–0.91) 0.88 (0.82–0.94) Specificity 0.83 (0.75–0.90) 0.64 (0.54–0.73) Positive predictive value 0.97 (0.94–1.00) 0.88 (0.81–0.94) Negative predictive value 0.45 (0.35–0.54) 0.65 (0.55–0.74) False-positive rate 0.17 (0.10–0.25) 0.36 (0.27–0.46) False-negative rate 0.16 (0.09–0.23) 0.12 (0.06–0.18) Accuracy 0.84 (0.77–0.91) 0.82 (0.74–0.89) AUC 0.81 (0.74–0.87) 0.82 (0.76–0.88) Abbreviations: MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; AUC, area under the curve; CI, confidence interval Univariate analysis of factors correlated with pCR revealed that PgR positivity and findings indicating cCR on interim and post-NAC MRI were significant predictors of pCR (Table 3 ). In the multivariate analysis, PgR and cCR on interim and post-NAC MRI remained significant predictors, with cCR on interim MRI being the most significant predictor of pCR (Table 4 ). Table 3 Univariate analysis of predictors of pCR Factors Favorable Unfavorable Odds ratio (95% CI) P Age (years) < 58 ≥ 58 0.05 (0.01–0.23) 0.81 Clinical T-factor T1 T2, T3 0.06 (0.05–0.88) 0.38 Clinical N-factor N0 N1, N2, N3 0.05 (0.04–0.74) 0.46 ER Positive Negative 0.07 (0.06–0.88) 0.38 PgR Positive Negative 0.16 (0.06–2.46) 0.01 * HER2 Positive Negative 0.06 (0.05–1.22) 0.22 Ki-67 < 20 ≥ 20 0.07 (0.03–0.39) 0.70 Nuclear grade 1, 2 3 0.07 (0.05–1.44) 0.15 Ly ly0 ly1, ly2 0.06 (0.01–0.22) 0.83 V v0 v1, v2 NA NA Interim MRI cCR Non-cCR 0.46 (0.09–5.18) < 0.0001 *** Post-NAC MRI cCR Non-cCR 0.18 (0.07–2.53) 0.01 * Recurrence No Yes 0.13 (0.09–1.44) 0.15 *P < 0.05, *** P < 0.001 Abbreviations: cCR, clinical complete response; ER, estrogen receptor; PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; CI, confidence interval Table 4 Multivariate analysis of predictors of pCR Factors Favorable Unfavorable Odds ratio (95% CI) p Multivariate analysis Age (years) < 58 ≥ 58 0.29 (0.14–0.46) 0.77 Clinical T-factor T1 T2, T3 0.50 (0.40–0.81) 0.42 Clinical N-factor N0 N1, N2, N3 0.44 (0.30–0.68) 0.50 ER Positive Negative 0.53 (0.45–0.84) 0.40 PgR Positive Negative 1.35 (0.57–2.39) 0.02 * HER2 Positive Negative 0.62 (0.44–1.41) 0.16 Ki-67 < 20 ≥ 20 0.73 (0.65–1.11) 0.51 Nuclear grade 1,2 3 0.77 (0.54–1.42) 0.16 Ly ly0 ly1, ly2 0.46 (0.20–0.43) 0.67 V v0 v1, v2 NA NA Interim MRI cCR Non-cCR 2.47 (0.70–3.5) < 0.01 *** Post-NAC MRI cCR Non-cCR 1.10 (0.53–2.07) 0.04 * Recurrence No Yes 1.15 (0.99–1.16) 0.25 *P < 0.05, *** P < 0.001 Abbreviations: cCR, clinical complete response. ER, estrogen receptor. PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; MRI, magnetic resonance imaging’ NAC, neoadjuvant chemotherapy; CI, confidence interval cCR on interim MRI showed high diagnostic accuracy for all subtypes (Table 5 ). It showed high sensitivity (95%) but low specificity (50%) for the luminal type. In contrast, for HER2 and TNBC types, interim MRI had slightly lower sensitivity (77% and 73%, respectively) and higher specificity (94% and 89%, respectively). Table 5 Diagnostic accuracy of interim MRI and post-NAC MRI by clinical subtypes Interim MRI (95% CI) Post-NAC MRI (95% CI) Luminal N = 107 HER2-type N = 83 TNBC N = 70 Luminal N = 107 HER2-type N = 83 TNBC N = 70 Sensitivity 0.95 (0.91–0.99) 0.77 (0.69–0.85) 0.73 (0.65–0.82) 0.98 (0.95–1.01) 0.82 (0.74–0.90) 0.78 (0.70–0.86) Specificity 0.50 (0.40–0.60) 0.94 (0.90–1.00) 0.89 (0.83–0.95) 0.41 (0.32–0.51) 0.70 (0.61–0.79) 0.75 (0.67–0.83) Positive predictive value 0.96 (0.92–1.00) 0.98 (0.95–1.01) 0.98 (0.95–1.01) 0.90 (0.84–0.96) 0.80 (0.73–0.88) 0.91 (0.86–0.97) Negative predictive value 0.44 (0.35–0.54) 0.53 (0.43–0.63) 0.33 (0.24–0.43) 0.22 (0.14–0.30) 0.28 (0.19–0.37) 0.50 (0.40–0.60) False-positive rate 0.50 (0.40–0.60) 0.06 (0.01–0.10) 0.11 (0.05–0.17) 0.59 (0.49–0.68) 0.30 (0.21–0.39) 0.25 (0.17–0.33) False-negative rate 0.05 (0.01–0.09) 0.23 (0.15–0.31) 0.27 (0.18–0.35) 0.02 (0.01–0.05) 0.18 (0.10–0.26) 0.22 (0.14–0.30) Accuracy 0.92 (0.86–0.97) 0.81 (0.73–0.88) 0.75 (0.67–0.84) 0.89 (0.83–0.95) 0.77 (0.69–0.85) 0.77 (0.69–0.85) AUC 0.86 (0.73–1.00) 0.80 (0.68–0.92) 0.75 (0.62–0.88) 0.91 (0.84–0.98) 0.80 (0.68–0.90) 0.77 (0.65–0.89) Abbreviations: MRI, magnetic resonance imaging; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; CI, confidence interval Of the 260 cases, 35 were diagnosed as cCR by interim MRI, of which 29 (83%) eventually achieved pCR (Fig. 2 ). When limited to the HER2 and TNBC types, 25 (93%) of the 27 cases defined as cCR achieved pCR. Of the 225 cases defined as non-cCR based on interim MRI, 189 (84%) did not achieve pCR. When limited to the luminal type, 94 (95%) of the 99 cases defined as non-cCR by interim MRI also did not achieve pCR. Discussion In HER2- and TNBC-type breast cancer, NAC is the main line of treatment, given that it has prognostic value (depending on whether pCR is obtained by NAC) and that it aids in decision-making regarding changing postoperative treatment when preoperative results are ineffective [ 18 – 20 ]. Recent studies have reported high pCR rates in patients with a poor response to the initial chemotherapy after the drug was changed; consequently, attempts are being made to determine and predict drug response earlier [ 12 ]. If efficacy can be determined early in the regimen, patients who are or are not likely to achieve pCR can be predicted, and thus, whether the course of subsequent treatment needs to be changed can be determined [ 12 ]. We hypothesized that the efficacy of NAC could be predicted using contrast-enhanced breast MRI during the interim phase of NAC. We found that interim MRI showed comparable diagnostic accuracy to that of post-NAC MRI and that cCR on interim MRI was the most accurate predictor of eventual pCR in both univariate and multivariate regression analyses. When examined by clinical subtype, interim MRI had high sensitivity and low specificity for the luminal type and conversely had high specificity and low sensitivity for HER2 and TNBC types. Therefore, we propose that interim MRI may be useful for predicting pCR in HER2 and TNBC types and for predicting non-pCR in luminal-type breast cancer. In fact, 93% of HER2-type and TNBC cases diagnosed as cCR on interim MRI were eventually confirmed as pCR, and 95% of the luminal type diagnosed as non-cCR on interim MRI I were confirmed as non-pCR. Based on these results, it is possible to reduce the extent of resection or even omit surgery in patients with HER2 and TNBC types who are undergoing NAC and who are diagnosed as cCR on interim MRI. If the ongoing Japan Clinical Oncology Group (JCOG) 1806 trial shows positive results, interim MRI may become important for selecting patients in whom surgery can be omitted with great certainty [ 7 , 21 ]. For patients with luminal-type breast cancer who are found to be non-cCR on interim MRI, aggressive modification of the last half of the NAC regimen may markedly improve therapeutic efficacy. In the past, anthracycline–taxane was the only option for luminal-type breast cancer. However, in future, adding angiogenesis inhibitors or immune checkpoint inhibitors may improve treatment outcomes of these patients [ 22 ]. Thus, predicting treatment response in the early phase could improve outcomes in all subtypes and may eliminate overtreatment. Rather than developing a novel imaging device, our study showed that outcomes for breast cancer patients could be improved by formulating an examination schedule and combining features of clinical subtypes and the existing imaging modalities. However, our study had several limitations. First, our study was a retrospective analysis, which may have resulted in patient selection bias. Second, immune checkpoint inhibitors are now increasingly used as preoperative chemotherapy for TNBC, which implies a need for further study. Third, the number of patients (n = 260) in this study, while the largest reported to date, was relatively small when analyzed separately for each subtype. Thus, further accumulation of patient data is required. In conclusion, based on our results, interim MRI has a diagnostic accuracy for pCR that is comparable to that of post-NAC MRI in breast cancer patients undergoing NAC and can predict drug treatment efficacy in the early phase. It is likely that treatment strategies based on early NAC efficacy determination via interim MRI will improve treatment outcomes of breast cancer patients in the future. Abbreviations AUC, area under the curve cCR, clinical complete response ER, estrogen receptor HR, hormone receptor MRI, magnetic resonance imaging NAC, neoadjuvant chemotherapy NPV, negative predictive value pCR, pathological complete response PET-CT, positron emission tomography–computed tomography PgR, progesterone receptor PPV, positive predictive value TNBC, triple-negative breast cancer HER2, human epidermal growth factor receptor 2 FEC, 5-fluorouracil, epidoxorubicin and cyclophosphamide Declarations Statements & Declarations Funding : The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing interests: The authors have no relevant financial or non-financial interests to disclose. Author contributions : All authors contributed to the study conception and design. Material preparation, and data collection and analysis were performed by Eri Kato and Takayuki Kadoya. The first draft of the manuscript was written by Eri Kato, and all authors commented on versions of the manuscript. All authors read and approved the final manuscript. Data availability statement : All data relevant to the study are included in this paper Ethics approval : Approval was obtained from the ethics committee of Hiroshima University Hospital. The procedures used in this study adhered to the tenets of the Declaration of Helsinki. Consent to participate : Patients provided informed consent to participate in this study. Consent to publish: Not applicable Code availability: Not applicable References Tudorica A, Oh KY, Chui SY et al (2016) Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. 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Front Oncol 13:1264259. https://doi.org/10.3389/fonc.2023.1264259 Pelekanou V, Barlow WE, Nahleh ZA et al (2018) Tumor-infiltrating lymphocytes and PD-L1 expression in pre- and posttreatment breast cancers in the SWOG S0800 Phase II Neoadjuvant Chemotherapy Trial. Mol Cancer Ther 17:1324–1331. https://doi.org/10.1158/1535-7163.Mct-17-1005 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4230661","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288577590,"identity":"ecebedd8-11f0-4e29-889f-7b0c52a1a03f","order_by":0,"name":"Eri Kato","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Eri","middleName":"","lastName":"Kato","suffix":""},{"id":288577591,"identity":"c0e3984c-7b31-47bd-a4c4-8199c0e98b3e","order_by":1,"name":"Shinsuke Sasada","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Shinsuke","middleName":"","lastName":"Sasada","suffix":""},{"id":288577592,"identity":"451624a3-4a19-483f-8f94-6893c996b391","order_by":2,"name":"Norio Masumoto","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Norio","middleName":"","lastName":"Masumoto","suffix":""},{"id":288577593,"identity":"8dc22ae5-155b-4309-8b37-3eb0aab9fd00","order_by":3,"name":"Akiko Emi","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Akiko","middleName":"","lastName":"Emi","suffix":""},{"id":288577594,"identity":"09eb633c-669f-4617-a78a-7fe616633bec","order_by":4,"name":"Hideo Shigematsu","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Hideo","middleName":"","lastName":"Shigematsu","suffix":""},{"id":288577595,"identity":"5cd378cc-d27e-4f70-8307-7e7eb48a5c2d","order_by":5,"name":"Morihito Okada","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Morihito","middleName":"","lastName":"Okada","suffix":""},{"id":288577596,"identity":"952a4ea3-4de0-46d3-82ef-fab07b29736f","order_by":6,"name":"Takayuki Kadoya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACNhCRwCPBwGAAZNgAMWPjAXwaeNC0pIG0NBClhQGq5TBYFK8We/ZjiZ9529JkJNsPb3zMU3Pebm37YaAtNTbROG3hSTsszduWwyPNk1ZszHPsdvK2M4lALcfSchtwOiy9AailgkeOIcdMmoftdrLZAaAWxobDuLXwP2/+DdbC/wao5d+5ZLPzDwlokUg7BnGYBNAW3rYDdmY3CNly41ma5ZxzaTySM54VG87tS04wuwG0JQGPX9j704xvvClLtpc4n7zxwZtvdvZm59MfPvhQY4NTCwgw8SAxEsEqE/AoBwHGH0gMewKKR8EoGAWjYAQCAJXyXkEwtIlwAAAAAElFTkSuQmCC","orcid":"","institution":"Shimane University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Takayuki","middleName":"","lastName":"Kadoya","suffix":""}],"badges":[],"createdAt":"2024-04-07 09:30:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4230661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4230661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54593978,"identity":"38878fa3-1476-4fe0-889c-0199299137ee","added_by":"auto","created_at":"2024-04-12 18:19:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":476212,"visible":true,"origin":"","legend":"\u003cp\u003eExample of evaluation of MRI findings\u003c/p\u003e\n\u003cp\u003e(a) Interim MRI: non-cCR, Post-NAC: non-cCR. (b) Interim MRI: non-cCR, Post-NAC: cCR. (c) Interim\u003c/p\u003e\n\u003cp\u003eMRI: cCR, Post-NAC: cCR.\u003c/p\u003e\n\u003cp\u003eAbbreviations: MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; cCR, clinical complete response\u003c/p\u003e","description":"","filename":"interimcontrastenhancedbreastMRIFigure1ERIKATO1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4230661/v1/c73d2f524c70c56ffc679abb.jpg"},{"id":54594393,"identity":"646170f2-36c0-45a3-ba07-b9d833b99321","added_by":"auto","created_at":"2024-04-12 18:27:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370465,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and trial profile of patients who received NAC\u003c/p\u003e\n\u003cp\u003eAbbreviations: MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; HER2, human epidermal growth factor receptor 2; FEC, 5-fluorouracil, epidoxorubicin and cyclophosphamide\u003c/p\u003e","description":"","filename":"interimcontrastenhancedbreastMRIFigure1ERIKATO2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4230661/v1/af7dd98d568413dea230756b.jpg"},{"id":54593976,"identity":"4ad67b63-438b-46e9-b63c-67124104adb4","added_by":"auto","created_at":"2024-04-12 18:19:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":427111,"visible":true,"origin":"","legend":"\u003cp\u003ePathological response of cCR and non-cCR cases diagnosed using interim MRI\u003c/p\u003e\n\u003cp\u003eAbbreviations: MRI, magnetic resonance imaging; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; cCR, clinical complete response; pCR, pathologic complete response\u003c/p\u003e","description":"","filename":"interimcontrastenhancedbreastMRIFigure1ERIKATO3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4230661/v1/f2eec4b64b92713c304fb8f3.jpg"},{"id":61511628,"identity":"00535ac4-5fff-4794-b1b6-2eb9af928131","added_by":"auto","created_at":"2024-07-31 14:55:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4230661/v1/db005ec9-bb19-412f-b900-df9ae617f307.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Usefulness of interim contrast-enhanced breast MRI for predicting response of breast cancer to neoadjuvant chemotherapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeoadjuvant chemotherapy (NAC) is one of the standard treatments for breast cancer. It reduces the extent of surgical resection required and can indicate the sensitivity of cancer to anti-cancer drugs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In human epidermal growth factor receptor 2 (HER2)-positive breast cancer and triple-negative breast cancer (TNBC), NAC typically achieves a pathological complete response (pCR) rate as high as 50\u0026ndash;80%. As patients who achieve pCR by NAC have a good prognosis, NAC is particularly important in HER2-positive and TNBC cases. Hence, knowing the anti-cancer drug sensitivity is important to optimize the treatment plan and improve patient outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe main imaging modalities used to determine the effectiveness of NAC are ultrasonography, positron emission tomography\u0026ndash;computed tomography (PET-CT), and contrast-enhanced breast magnetic resonance imaging (MRI) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these modalities, contrast-enhanced MRI can accurately delineate the extent of breast cancer and is useful in determining the extent of surgical resection required [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the appropriate timing of such imaging for determining response at an early stage remains inconclusive. Such imaging might be conducted after two cycles of chemotherapy when using PET-CT or after one or two courses of chemotherapy when using contrast-enhanced breast MRI [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The timing of efficacy determination and the imaging modalities used in cases undergoing NAC are crucial as omitting surgery can be considered in cases where pCR is predicted [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Currently, imaging is conducted only once NAC is completed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, early efficacy testing can result in improved pCR rates as it would allow changes in the NAC regimen in cases showing poor response to the initial NAC used [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe considered that contrast-enhanced breast MRI in patients undergoing NAC conducted during the interim period might predict treatment response at an early stage. Here, we reported a detailed retrospective analysis of the efficacy of interim MRI to reflect response to treatment, including that in distinct clinical subtypes of breast cancer.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA total of 260 consecutive patients with invasive breast cancer who underwent NAC at Hiroshima University Hospital between April 2010 and December 2020 and who had undergone breast MRI before, during, and after completion of NAC were included in this study. Patients essentially received four courses each of taxane (4 cycles docetaxel 75 mg/m\u003csup\u003e2\u003c/sup\u003e) and anthracycline (4 cycles epirubicin 90 mg/m\u003csup\u003e2\u003c/sup\u003e, cyclophosphamide 600 mg/m\u003csup\u003e2\u003c/sup\u003e) chemotherapy combined with anti-HER2 therapy in cases of HER2-positive breast cancer. We excluded patients aged below 18 years, those who were not prescribed NAC, and those who did not undergo contrast-enhanced breast MRI.\u003c/p\u003e \u003cp\u003e The study followed the Helsinki Declaration guidelines and was approved by the Institutional Review Board of Hiroshima University Hospital (approval number E2014-1157-06). Patients provided informed consent to participate in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePathological diagnosis\u003c/h2\u003e \u003cp\u003eIn this study, pCR was defined as the absence of invasive disease or intraepithelial ductal carcinoma in the breast and lymph nodes (ypT0ypN0). HER2 positivity was defined as HER2 3\u0026thinsp;+\u0026thinsp;or HER2 2\u0026thinsp;+\u0026thinsp;and fluorescent in situ hybridization-positive staining [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hormone receptor (HR)-positive cases were considered luminal breast cancers. Cases with positive estrogen receptor (ER) and progesterone receptor (PgR) staining of \u0026ge;\u0026thinsp;1% were considered ER or PgR positive, respectively. HR and HER2 status were assessed according to the guidelines of the American Society of Clinical Oncology/College of American Pathologists [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The molecular subtypes of breast cancer were classified as luminal (ER+/ HER2-), HER2-type (ER+/HER2+, ER-/HER2+), or TNBC (ER-/HER2-).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eContrast-enhanced breast MRI\u003c/h2\u003e \u003cp\u003eAll patients underwent contrast-enhanced breast MRI with a 1.5-T 7-channel table plane coil (Philips Achieva and Philips Ingenia, respectively; Philips Healthcare, Best, The Netherlands) before, during (after four courses of NAC), and after completion of NAC. Images were evaluated in a multidisciplinary meeting that included a breast surgeon, pathologist, radiologist, and ultrasound examination technician. For axial plane images, the contrast media used were 0.1 mmol/kg dimeglumine gadopentetate (Magnevist; Bayer, Osaka, Japan) or gadobutrol (Gadovist; Bayer). In addition, a 3D gradient echo sequence (repetition time/echo time, 3.9/1.9 ms; flip angle, 10\u0026deg;; field-of-view: diffusion-weighted imaging, 2.73 mm/3.46 mm/5.0 mm; T1-weighted imaging, 1.00 mm/1.14 mm/5.0 mm; T2-weighted fat-suppressed, 0.95 mm/1.33 mm/5.0 mm, dynamic, 1.05 mm/1.17 mm/1.6 mm; T1-weighted fat-suppressed sagittal, 0.9 mm/0.9 mm/2.0 mm) was used to acquire contrast-enhanced unilateral sagittal images, with high spatial resolution, of the cancer-affected and contralateral breasts. The images were acquired at 70, 140, 180, and 320 s after contrast injection.\u003c/p\u003e \u003cp\u003eComplete response on contrast-enhanced breast MRI was defined as the absence of contrast enhancement in the early and late nonsubtracted fat-suppressed dynamic contrast-enhanced axial images or a faint enhancement that was less than or equal to that of normal mammary glands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Clinical complete response (cCR) was evaluated on interim and post-NAC MRI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor this study, high sensitivity was defined as the ability to diagnose residual lesions. The software program JMP\u003csup\u003e\u0026reg;\u003c/sup\u003e Pro 16 (SAS Institute, Cary, NC, USA) was used for statistical analyses to calculate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and test accuracy. Univariate and multivariate analyses were performed using logistic regression analysis. Area under the curve (AUC) was used to assess the discriminative power of the model. A significance level of p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe characteristics of the 260 participants included in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients\u0026rsquo; mean age was 52\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years. Most patients had stage II cancer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In terms of clinical subtypes, luminal type accounted for the most cases, followed by the HER2 type and then TNBC. pCR was achieved by NAC in one-quarter of patients, predominantly in those with the HER2 type and TNBC.\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\u003eCharacteristics of patients included in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;260 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (22\u0026ndash;72) years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67 labeling index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: NAC, neoadjuvant chemotherapy; pCR, pathologic complete response; SD, standard deviation; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe regimens of NAC used for the 260 patients are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These included taxane to 5-fluorouracil, epidoxorubicin, and cyclophosphamide (FEC) in 96 patients, FEC to taxane in 49 patients, taxane\u0026thinsp;+\u0026thinsp;anti-HER2 drug to FEC in 59 patients, FEC to taxane\u0026thinsp;+\u0026thinsp;anti-HER2 drug in 7 patients, and other regimens in 49 patients. Most patients with HER2-positive breast cancer had received prior taxane\u0026thinsp;+\u0026thinsp;anti-HER2 drugs.\u003c/p\u003e \u003cp\u003eThe accuracy of interim and post-NAC MRI in predicting pCR were similar and exceeded 80% in both cases. The sensitivity, specificity, PPV, NPV, and accuracy of both approaches are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The AUC was 0.81 for interim MRI and 0.82 for post-NAC MRI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy of interim MRI and post-NAC MRI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterim MRI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-NAC MRI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.77\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.82\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.75\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.54\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.94\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.81\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.35\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.55\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalse-positive rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17 (0.10\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36 (0.27\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalse-negative rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.09\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12 (0.06\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.77\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.74\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.74\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.76\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; AUC, area under the curve; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate analysis of factors correlated with pCR revealed that PgR positivity and findings indicating cCR on interim and post-NAC MRI were significant predictors of pCR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the multivariate analysis, PgR and cCR on interim and post-NAC MRI remained significant predictors, with cCR on interim MRI being the most significant predictor of pCR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of predictors of pCR\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 \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfavorable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (0.01\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical T-factor\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2, T3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06 (0.05\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical N-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN1, N2, N3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (0.04\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.06\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16 (0.06\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06 (0.05\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.03\u0026ndash;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.05\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ely0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ely1, ly2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06 (0.01\u0026ndash;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ev1, v2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterim MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-cCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.09\u0026ndash;5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-NAC MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-cCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18 (0.07\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13 (0.09\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: cCR, clinical complete response; ER, estrogen receptor; PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of predictors of pCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnfavorable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29 (0.14\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical T-factor\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\u003eT2, T3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.40\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical N-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN1, N2, N3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44 (0.30\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.45\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (0.57\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.44\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.65\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.54\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ely0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ely1, ly2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46 (0.20\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ev0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ev1, v2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterim MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-cCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47 (0.70\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-NAC MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-cCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.53\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.99\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: cCR, clinical complete response. ER, estrogen receptor. PgR, progesterone receptor; HER2, human epidermal growth factor receptor 2; MRI, magnetic resonance imaging\u0026rsquo; NAC, neoadjuvant chemotherapy; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ecCR on interim MRI showed high diagnostic accuracy for all subtypes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It showed high sensitivity (95%) but low specificity (50%) for the luminal type. In contrast, for HER2 and TNBC types, interim MRI had slightly lower sensitivity (77% and 73%, respectively) and higher specificity (94% and 89%, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic accuracy of interim MRI and post-NAC MRI by clinical subtypes\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\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eInterim MRI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePost-NAC MRI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHER2-type\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHER2-type\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(0.91\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.65\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.74\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003cp\u003e(0.70\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.40\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003cp\u003e(0.32\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e(0.61\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e(0.67\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.86\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e(0.35\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003cp\u003e(0.43\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e(0.24\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003cp\u003e(0.14\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e(0.19\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.40\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalse-positive rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.40\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(0.01\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e(0.05\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003cp\u003e(0.49\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003cp\u003e(0.21\u0026ndash;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e(0.17\u0026ndash;0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalse-negative rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(0.01\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e(0.15\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003cp\u003e(0.18\u0026ndash;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(0.01\u0026ndash;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003cp\u003e(0.10\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003cp\u003e(0.14\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.86\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e(0.67\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e(0.83\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.68\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.68\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003cp\u003e(0.65\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: MRI, magnetic resonance imaging; HER2, human epidermal growth factor receptor 2; TNBC, triple-negative breast cancer; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the 260 cases, 35 were diagnosed as cCR by interim MRI, of which 29 (83%) eventually achieved pCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When limited to the HER2 and TNBC types, 25 (93%) of the 27 cases defined as cCR achieved pCR. Of the 225 cases defined as non-cCR based on interim MRI, 189 (84%) did not achieve pCR. When limited to the luminal type, 94 (95%) of the 99 cases defined as non-cCR by interim MRI also did not achieve pCR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn HER2- and TNBC-type breast cancer, NAC is the main line of treatment, given that it has prognostic value (depending on whether pCR is obtained by NAC) and that it aids in decision-making regarding changing postoperative treatment when preoperative results are ineffective [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Recent studies have reported high pCR rates in patients with a poor response to the initial chemotherapy after the drug was changed; consequently, attempts are being made to determine and predict drug response earlier [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. If efficacy can be determined early in the regimen, patients who are or are not likely to achieve pCR can be predicted, and thus, whether the course of subsequent treatment needs to be changed can be determined [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We hypothesized that the efficacy of NAC could be predicted using contrast-enhanced breast MRI during the interim phase of NAC. We found that interim MRI showed comparable diagnostic accuracy to that of post-NAC MRI and that cCR on interim MRI was the most accurate predictor of eventual pCR in both univariate and multivariate regression analyses. When examined by clinical subtype, interim MRI had high sensitivity and low specificity for the luminal type and conversely had high specificity and low sensitivity for HER2 and TNBC types. Therefore, we propose that interim MRI may be useful for predicting pCR in HER2 and TNBC types and for predicting non-pCR in luminal-type breast cancer. In fact, 93% of HER2-type and TNBC cases diagnosed as cCR on interim MRI were eventually confirmed as pCR, and 95% of the luminal type diagnosed as non-cCR on interim MRI I were confirmed as non-pCR.\u003c/p\u003e \u003cp\u003eBased on these results, it is possible to reduce the extent of resection or even omit surgery in patients with HER2 and TNBC types who are undergoing NAC and who are diagnosed as cCR on interim MRI. If the ongoing Japan Clinical Oncology Group (JCOG) 1806 trial shows positive results, interim MRI may become important for selecting patients in whom surgery can be omitted with great certainty [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For patients with luminal-type breast cancer who are found to be non-cCR on interim MRI, aggressive modification of the last half of the NAC regimen may markedly improve therapeutic efficacy. In the past, anthracycline\u0026ndash;taxane was the only option for luminal-type breast cancer. However, in future, adding angiogenesis inhibitors or immune checkpoint inhibitors may improve treatment outcomes of these patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, predicting treatment response in the early phase could improve outcomes in all subtypes and may eliminate overtreatment.\u003c/p\u003e \u003cp\u003eRather than developing a novel imaging device, our study showed that outcomes for breast cancer patients could be improved by formulating an examination schedule and combining features of clinical subtypes and the existing imaging modalities. However, our study had several limitations. First, our study was a retrospective analysis, which may have resulted in patient selection bias. Second, immune checkpoint inhibitors are now increasingly used as preoperative chemotherapy for TNBC, which implies a need for further study. Third, the number of patients (n\u0026thinsp;=\u0026thinsp;260) in this study, while the largest reported to date, was relatively small when analyzed separately for each subtype. Thus, further accumulation of patient data is required.\u003c/p\u003e \u003cp\u003eIn conclusion, based on our results, interim MRI has a diagnostic accuracy for pCR that is comparable to that of post-NAC MRI in breast cancer patients undergoing NAC and can predict drug treatment efficacy in the early phase. It is likely that treatment strategies based on early NAC efficacy determination via interim MRI will improve treatment outcomes of breast cancer patients in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC, area under the curve\u003c/p\u003e\n\u003cp\u003ecCR, clinical complete response\u003c/p\u003e\n\u003cp\u003eER, estrogen receptor\u003c/p\u003e\n\u003cp\u003eHR, hormone receptor\u003c/p\u003e\n\u003cp\u003eMRI, magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eNAC, neoadjuvant chemotherapy\u003c/p\u003e\n\u003cp\u003eNPV, negative predictive value\u003c/p\u003e\n\u003cp\u003epCR, pathological complete response\u003c/p\u003e\n\u003cp\u003ePET-CT, positron emission tomography\u0026ndash;computed tomography\u003c/p\u003e\n\u003cp\u003ePgR, progesterone receptor\u003c/p\u003e\n\u003cp\u003ePPV, positive predictive value\u003c/p\u003e\n\u003cp\u003eTNBC, triple-negative breast cancer\u003c/p\u003e\n\u003cp\u003eHER2,\u0026nbsp;human epidermal growth factor\u0026nbsp;receptor\u0026nbsp;2\u003c/p\u003e\n\u003cp\u003eFEC,\u0026nbsp;5-fluorouracil, epidoxorubicin and cyclophosphamide\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements \u0026amp; Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: All authors contributed to the study conception and design. Material preparation, and data collection and analysis were performed by Eri Kato and Takayuki Kadoya. The first draft of the manuscript was written by Eri Kato, and all authors commented on versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: All data relevant to the study are included in this paper\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e:\u0026nbsp;Approval was obtained from the ethics committee of Hiroshima University Hospital. The procedures used in this study adhered to the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e: Patients provided informed consent to participate in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTudorica A, Oh KY, Chui SY et al (2016) Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. 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Mol Cancer Ther 17:1324\u0026ndash;1331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1535-7163.Mct-17-1005\u003c/span\u003e\u003cspan address=\"10.1158/1535-7163.Mct-17-1005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, dynamic contrast-enhanced magnetic resonance imaging, neoadjuvant chemotherapy, pathological response, sensitivity, specificity","lastPublishedDoi":"10.21203/rs.3.rs-4230661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4230661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIn breast cancer patients, contrast-enhanced breast magnetic resonance imaging (MRI) is usually performed after completing neoadjuvant chemotherapy (NAC) to determine treatment efficacy. We compared the ability of MRI conducted during the interim phase (interim MRI) with that of MRI conducted after NAC (post-NAC MRI) to predict treatment response.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective study, 260 patients with invasive breast cancer who underwent NAC between April 2010 and December 2020 and who had undergone MRI before, during, and after NAC were included. Most patients received taxane and anthracycline sequentially, while human epidermal growth factor receptor 2 (HER2)-positive cases also received anti-HER2 agents.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSixty-five patients (25%) demonstrated a pathological complete response (pCR). The sensitivity and specificity of interim MRI for predicting pCR in all patients were 84% and 83%, respectively, which were comparable with those of post-NAC MRI (88% and 64%, respectively). Its sensitivity and specificity in luminal-type breast cancer were 95% and 50%, respectively, and those in HER2-positive breast cancer were 77% and 73%, respectively, while those in triple-negative breast cancer (TNBC) cases were 94% and 89%, respectively. Twenty-nine (83%) of 35 cases diagnosed with clinical complete response (cCR) by interim MRI achieved pCR, whereas 25 (93%) of the 27 HER2-positive type or TNBC cases achieved pCR. Similarly, 189 (84%) of 225 cases indicated as non-cCR by interim MRI were non-pCR, whereas 94 (95%) of 99 non-cCR luminal-type cases were non-pCR.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eInterim MRI could predict treatment response at an early stage in breast cancer patients receiving NAC.\u003c/p\u003e","manuscriptTitle":"Usefulness of interim contrast-enhanced breast MRI for predicting response of breast cancer to neoadjuvant chemotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 18:19:03","doi":"10.21203/rs.3.rs-4230661/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f081b5a5-f600-4e27-b221-326299adb95e","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-31T14:47:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 18:19:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4230661","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4230661","identity":"rs-4230661","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}