Patient-Reported Outcomes of Omission of Breast Surgery Following Neoadjuvant Systemic Therapy: A Nonrandomized Clinical Trial

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification

BACKGROUND

The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early pCR prediction.

PURPOSE

In this study, we investigated the feasibility of the early prediction of pCR to neoadjuvant chemotherapy using multi-time point DCEMR images and clinical data acquired in the I-SPY2 trial. The prediction uncertainty was also quantified to allow physicians to make patient-specific decisions on treatment plans based on the level of associated uncertainty.

METHODS

The dataset used in our study included 624 patients with DCEMR images acquired at 3 time points before the completion of the chemotherapy: pretreatment (T0), after 3 cycles of treatment (T1), and after 12 cycles of treatment (T2). A convolutional long short-term memory (LSTM) network-based deep learning model, which integrated multi-time point deep image representations with clinical data, including tumor subtypes, was developed to predict pCR. The performance of the model was evaluated via the method of nested 5-fold cross validation. Moreover, we also quantified prediction uncertainty for each patient through test-time augmentation. To investigate the relationship between predictive performance and uncertainty, the area under the receiver operating characteristic curve (AUROC) was assessed on subgroups of patients stratified by the uncertainty score.

RESULTS

By integrating clinical data and DCEMR images obtained at three-time points before treatment completion, the AUROC reached 0.833 with a sensitivity of 0.723 and specificity of 0.800. This performance was significantly superior (p < 0.01) to models using only images (AUROC = 0.706) or only clinical data (AUROC = 0.746). After stratifying the patients into eight subgroups based on the uncertainty score, we found that group #1, with the lowest uncertainty, had a superior AUROC of 0.873. The AUROC decreased to 0.637 for group #8, which had the highest uncertainty.

CONCLUSIONS

The results indicate that our convolutional LSTM network-based deep learning model can be used to predict pCR earlier before the completion of chemotherapy. By combining clinical data and multi-time point deep image representations, our model outperforms models built solely on clinical or image data. Estimating prediction uncertainty may enable physicians to prioritize or disregard predictions based on their associated uncertainties. This approach could potentially enhance the personalization of breast cancer therapy.

MRI features of breast cancer immunophenotypes with a focus on luminal estrogen receptor low positive invasive carcinomas

To compare the magnetic resonance imaging (MRI) features of different immunophenotypes of breast carcinoma of no special type (NST), with special attention to estrogen receptor (ER)-low-positive breast cancer. This retrospective, single-centre, Institutional Review Board (IRB)-approved study included 398 patients with invasive breast carcinoma. Breast carcinomas were classified as ER-low-positive when there was ER staining in 1-10% of tumour cells. Pretreatment MRI was reviewed to assess the tumour imaging features according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. Of the 398 cases, 50 (12.6%) were luminal A, 191 (48.0%) were luminal B, 26 (6.5%) were luminal ER-low positive, 64 (16.1%) were HER2-overexpressing, and 67 (16.8%) were triple negative. Correlation analysis between MRI features and tumour immunophenotype showed statistically significant differences in mass shape, margins, internal enhancement and the delayed phase of the kinetic curve. An oval or round shape and rim enhancement were most frequently observed in triple-negative and luminal ER-low-positive tumours. Spiculated margins were most common in luminal A and luminal B tumours. A persistent kinetic curve was more frequent in luminal A tumours, while a washout curve was more common in the triple-negative, HER2-overexpressing and luminal ER-low-positive immunophenotypes. Multinomial regression analysis showed that luminal ER-low-positive tumours had similar results to triple-negative tumours for almost all variables. Luminal ER-low-positive tumours present with similar MRI findings to triple-negative tumours, which suggests that MRI can play a fundamental role in adequate radiopathological correlation and therapeutic planning in these patients.

Omission of Breast Surgery in Exceptional Responders

Breast cancer management has transformed significantly over the last decades, primarily through the integration of neoadjuvant systemic therapy (NST) and the evolving understanding of tumor biology, enabling more tailored treatment strategies. The aim of this review is to critically present the historical context and contemporary evidence surrounding the potential of omission of surgery post-NST, focusing on exceptional responders who have achieved a pathologic complete response (pCR). Identifying these exceptional responders before surgery remains a challenge, however standardized image-guided biopsy may allow optimized patient selection. The safety and feasibility of omitting breast and axillary surgeries in these exceptional responders are explored in ongoing clinical trials and the reported preliminary results appear promising. Moreover, understanding patient and physician perspectives regarding the potential elimination of surgery post-NST is integral. While some patients express a preference to omit or minimize surgery, the majority of healthcare providers are intrigued by the prospect of avoiding surgical interventions and endorse further research in this field.

Omission of Breast Surgery in Predicted Pathologic Complete Response after Neoadjuvant Systemic Therapy: A Multicenter, Single-Arm, Non-inferiority Trial

PURPOSE

Advances in chemotherapeutic and targeted agents have increased pathologic complete response (pCR) rates after neoadjuvant systemic therapy (NST). Vacuum-assisted biopsy (VAB) has been suggested to accurately evaluate pCR. This study aims to confirm the non-inferiority of the 5-year disease-free survival of patients who omitted breast surgery when predicted to have a pCR based on breast magnetic resonance imaging (MRI) and VAB after NST, compared with patients with a pCR who had undergone breast surgery in previous studies.

METHODS

The Omission of breast surgery for PredicTed pCR patients wIth MRI and vacuum-assisted bIopsy in breaST cancer after neoadjuvant systemic therapy (OPTIMIST) trial is a prospective, multicenter, single-arm, non-inferiority study enrolling in 17 tertiary care hospitals in the Republic of Korea. Eligible patients must have a clip marker placed in the tumor and meet the MRI criteria suggesting complete clinical response (post-NST MRI size ≤ 1 cm and lesion-to-background signal enhancement ratio ≤ 1.6) after NST. Patients will undergo VAB, and breast surgery will be omitted for those with no residual tumor. Axillary surgery can also be omitted if the patient was clinically node-negative before and after NST and met the stringent criteria of MRI size ≤ 0.5 cm. Survival and efficacy outcomes are evaluated over five years.

DISCUSSION

This study seeks to establish evidence for the safe omission of breast surgery in exceptional responders to NST while minimizing patient burden. The trial will address concerns about potential undertreatment due to false-negative results and recurrence as well as improved patient-reported quality of life issues from the omission of surgery. Successful completion of this trial may reshape clinical practice for certain breast cancer subtypes and lead to a safe and less invasive approach for selected patients.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05505357. Registered on August 17, 2022. Clinical Research Information Service Identifier: KCT0007638. Registered on July 25, 2022.