Dataset Information

Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy.

ABSTRACT:

Introduction

Nowadays chemotherapy in breast cancer patients is optionally applied neoadjuvant, which allows for testing of tumor response to the chemotherapeutical treatment in vivo, as well as allowing a greater number of patients to benefit from a subsequent breast-conserving surgery.

Material and methods

We compared breast ultrasonography, mammography, and clinical examination (palpation) results with postoperative histopathological findings after neoadjuvant chemotherapy, aiming to determine the most accurate prediction of complete remission and tumor-free resection margins. To this end, clinical and imaging data of 184 patients (193 tumors) with confirmed diagnosis of breast cancer and neoadjuvant therapy were analyzed.

Results

After chemotherapy, tumors could be assessed by palpation in 91.7%, by sonography in 99.5%, and by mammography in 84.5% (chi-square p < 0.0001) of cases. Although mammography proved more accurate in estimating the exact neoadjuvant tumor size than breast sonography in total numbers (136/163 (83.44%) vs. 142/192 (73.96%), n.s.), 29 tumors could be assessed solely by means of breast sonography. A sonographic measurement was feasible in 192 cases (99.48%) post-chemotherapy and in all cases prior to chemotherapy.

Conclusions

We determined a superiority of mammography and breast sonography over clinical palpation in predicting neoadjuvant tumor size. However, neither examination method can predict either pCR or tumor margins with high confidence.

SUBMITTER: Schmidt G

PROVIDER: S-EPMC10486727 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy.

Schmidt Gilda G Findeklee Sebastian S Del Sol Martinez Gerda G Georgescu Mihai-Teodor MT Gerlinger Christoph C Nemat Sogand S Klamminger Gilbert Georg GG Nigdelis Meletios P MP Solomayer Erich-Franz EF Hamoud Bashar Haj BH

Diagnostics (Basel, Switzerland) 20230830 17

<h4>Introduction</h4>Nowadays chemotherapy in breast cancer patients is optionally applied neoadjuvant, which allows for testing of tumor response to the chemotherapeutical treatment in vivo, as well as allowing a greater number of patients to benefit from a subsequent breast-conserving surgery.<h4>Material and methods</h4>We compared breast ultrasonography, mammography, and clinical examination (palpation) results with postoperative histopathological findings after neoadjuvant chemotherapy, aim ...[more]

PMID: 37685349

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Project description:ImportanceImage-guided breast biopsy of a residual imaging abnormality or tumor bed after neoadjuvant chemotherapy (NACT) is increasingly used to assess residual cancer, facilitate risk-adaptive surgery, and potentially identify exceptional responders in whom local therapy may be de-escalated.ObjectiveTo further assess the accuracy of post-NACT image-guided biopsy to predict residual cancer in the breast.Design, setting, and participantsThis diagnostic study analyzed multicenter patient-level data of patients with breast cancer who were treated with NACT and underwent image-guided biopsy before surgery at Royal Marsden Hospital in London, UK; Seoul National University Hospital in Seoul, South Korea; and MD Anderson Cancer Center in Houston, Texas. Data were analyzed from June to July 2019.Main outcomes and measuresDiagnostic accuracy of post-NACT image-guided biopsy. Final surgical pathology was used as reference standard.ResultsData from 166 women were analyzed. The median (range) age was 49 (25-76) years. The median (range) tumor size on pretreatment and posttreatment imaging was 33.5 (12-100) mm and 10 (0-100) mm, respectively. The overall pathologic complete response rate was 51.2% (n = 85) (16.1% [5 of 31] for hormone receptor-positive/ERBB2 (formerly HER2)-negative; 44.7% [21 of 47] for hormone receptor-positive/ERBB2-positive; 69% [20 of 29] for hormone receptor-negative/ERBB2-positive; and 66.1% [39 of 59] for triple negative). The majority (143 [86.1%]) underwent image-guided vacuum-assisted biopsy (VAB), and 23 had core-cut biopsy. The median (range) needle gauge was 10 (7-14), and the median (range) number of samples was 6 (2-18). When image-guided biopsy (VAB and core-cut biopsy) was representative (159 [95.8%]), the false-negative rate across the whole cohort was 18.7% (95% CI, 10.6%-29.3%). Subgroup analysis of patients with a complete/partial clinical response and residual imaging abnormality of 2 cm or smaller with at least 6 VABs taken (76 [45.8%]) demonstrated a false-negative rate of 3.2% (95% CI, 0.1%-16.7%), a negative predictive value of 97.4% (95% CI, 86.5%-99.9%), and an overall accuracy of 89.5% (95% CI, 80.3%-95.3%).Conclusions and relevanceThis large multicenter pooled data analysis suggests that a standardized protocol using image-guided VAB of a tumor bed measuring 2 cm or smaller with 6 or more representative samples allows reliable prediction of residual disease. These results could inform the design of de-escalation trials in NACT exceptional responders testing the safety of eliminating surgery.

Project description:BackgroundAxillary surgical management in patients with node-positive breast cancer at the time of diagnosis converted to negative nodes through neoadjuvant chemotherapy (NAC) remains unclear. Removal of more than two sentinel nodes (SLNs) in these patients may decrease the false negative rate (FNR) of sentinel lymph node biopsies (SLNBs). We aim to analyse the detection rate (DR) and the FNR of SLNB assessment according to the number of SLNs removed.MethodsA retrospective study was performed from October 2012 to December 2018. Patients with invasive breast cancer who had a clinically node-positive disease at diagnosis and with a complete axillary response after neoadjuvant chemotherapy were selected. Patients included underwent SLNB and axillary lymph node dissection (ALND) after NAC. The SLN was considered positive if any residual disease was detected. Descriptive statistics were used to describe the clinicopathologic features and the results of SLNB and ALND. The DR of SLNB was defined as the number of patients with successful identification of SLN. Presence of residual disease in ALND and negative SLN was considered false negative.ResultsA total of 368 patients with invasive breast cancer who underwent surgery after complete NAC were studied. Of them, 85 patients met the eligibility criteria and were enrolled in the study. The mean age at diagnosis was 50.8 years. Systematic lymphadenectomy was performed in all patients, with an average of 10 lymph nodes removed. The DR of SLNB was 92.9%, and the FNR was 19.1. The median number of SLNs removed was 3, and at least, three SLNs were obtained in 42 patients (53.2%). When at least three sentinel nodes were removed, the FNR decreased to 8.7%.ConclusionsIn this cohort, the SLN assessment was associated with an adequate DR and a high FNR. Removing three or more SLNs decreased the FNR from 19.1% to 8.7%. Complementary approaches may be considered for axillary lymph node staging after neoadjuvant chemotherapy. The study was approved by our institution's ethics committee (Instituto de Investigacion Sanitaria Hospital 12 de Octubre (imas12), Universidad Complutense de Madrid, Madrid, Spain) (https://clinicaltrials.gov/ct2/show/NCEI:20/0048).

Project description:BackgroundInvasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy.MethodsIn this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs).ResultsWe developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts.ConclusionThe proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

Project description:PurposeTo evaluate whether changes in ultrasound features during neoadjuvant chemotherapy (NAC) could predict axillary node response in clinically node-positive breast cancer patients.MethodsPatients with biopsy-proven node-positive disease receiving NAC between February 2009 and March 2021 were included. Ultrasound (US) images were obtained using a 5-12-MHz linear array transducer before NAC, after two cycles, and at the completion of NAC. Long and short diameter, cortical thickness, vascularity, and hilum status of the metastatic node were retrospectively reviewed according to breast imaging-reporting and data system (BI-RADS). The included population was randomly divided into a training set and a validation set at a 2:1 ratio using a simple random sampling method. Factors associated with node response were identified through univariate and multivariate analyses. A nomogram combining clinical and changes in ultrasonographic (US) features was developed and validated. The receiver operating characteristic (ROC) and calibration plots were applied to evaluate nomogram performance and discrimination.ResultsA total of 296 breast cancer patients were included, 108 (36.5%) of whom achieved axillary pathologic complete response (pCR) and 188 (63.5%) had residual nodal disease. Multivariate regression indicated that independent predictors of node pCR contain ultrasound features in addition to clinical features, clinical features including neoadjuvant HER2-targeted therapy and clinical response, ultrasound features after NAC including cortical thickness, hilum status, and reduction in short diameter ≥50%. The nomogram combining clinical features and US features showed better diagnostic performance compared to clinical-only model in the training cohort (AUC: 0.799 vs. 0.699, P=0.001) and the validation cohort (AUC: 0.764 vs. 0.638, P=0.027).ConclusionsUltrasound changes during NAC could improve the accuracy to predict node response after NAC in clinically node-positive breast cancer patients.

Dataset Information

Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy.

Introduction

Material and methods

Results

Conclusions

Publications

Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy.

Similar Datasets

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