A Model to Predict Upstaging to Invasive Carcinoma in Patients Preoperatively Diagnosed with Low-Grade Ductal Carcinoma In Situ of the Breast

Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

AIMS

Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low-risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high-risk lesions. We aimed to develop a machine-learning algorithm based on whole-slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision.

METHODS AND RESULTS

Patients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI-RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide- and patient-level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high-grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81).

CONCLUSION

This study demonstrated that deep-learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning.

Updates on Breast Cancer

This collection of 18 articles, comprising 12 original studies, 1 systematic review, and 5 reviews, is a collaborative effort by distinguished experts in breast cancer research, and it has been edited by Dr [...].

Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models

Objective

The purpose of this study was to build machine learning models to predict surgical upstaging risk of ductal carcinoma in situ (DCIS) to invasive cancer and to compare model performance to eligibility criteria used by the Comparison of Operative versus Monitoring and Endocrine Therapy (COMET) active surveillance trial.

Methods

Medical records were retrospectively reviewed of all women with DCIS at core-needle biopsy who underwent surgery from 2007 to 2016 at an academic medical center. Multivariable regression and machine learning models were developed to evaluate upstaging-related features and their performance was compared with that achieved using the COMET trial eligibility criteria.

Results

Of 1387 women (mean age, 57 years; range, 27-89 years), the upstaging rate of DCIS was 17% (235/1387). On multivariable analysis, upstaging-associated features were presentation of DCIS as a palpable area of concern, imaging finding of a mass, and nuclear grades 2 or 3 at biopsy (P < 0.05). If COMET trial eligibility criteria were applied to our study cohort, then 496 women (42%, 496/1175) would have been eligible for the trial, with an upstaging rate of 12% (61/496). Of the machine learning models, none had a significantly lower upstaging rate than 12%. However, if using the models to determine eligibility, then a significantly larger proportion of women (56%-87%) would have been eligible for active surveillance.

Conclusion

Use of machine learning models to determine eligibility for the COMET trial identified a larger proportion of women eligible for surveillance compared with current eligibility criteria while maintaining similar upstaging rates.

Management of Intraductal Papilloma of the Breast Diagnosed on Core Needle Biopsy: Latest Controversies

Intraductal papillomas (IDPs), including central papilloma and peripheral papilloma, are common in the female population. Due to the lack of specific clinical manifestations of IDPs, it is easy to misdiagnose or miss diagnose. The difficulty of differential diagnosis using imaging techniques also contributes to these conditions. Histopathology is the gold standard for the diagnosis of IDPs while the possibility of under sample exists in the percutaneous biopsy. There have been some debates about how to treat asymptomatic IDPs without atypia diagnosed on core needle biopsy (CNB), especially when the upgrade rate to carcinoma is considered. This article concludes that further surgery is recommended for IDPs without atypia diagnosed on CNB who have high-risk factors, while appropriate imaging follow-up may be suitable for those without risk factors.

Ductal carcinoma in situ of the male breast: clinical radiological features and management in a cancer referral center

Purpose

To present an overview of the management of male patients with Ductal Carcinoma In Situ of the breast (male DCIS).

Methods

We retrospectively studied all male patients with a diagnosis of pure DCIS from January 1999 to December 2018: 20 patients were identified in our cancer referral center. We collected data regarding clinical presentation, age of onset, radiological features, receptor status of the neoplasm, histological type, and the follow-up of those patients.

Results

The median age was 62 years (range 21–80). All patients underwent surgery, in 15/20 (75%) cases a mastectomy was carried out. Two patients (10%) underwent endocrine treatment and 1/20 (5%) underwent radiotherapy. The receptor status for 15/20 patients was documented: 13/15 patients were ER+/Pr+. In 3 cases the Ki 67% was positive (i.e., > 20%). All cases were negative for Her2. The median follow-up time was 9.0 years (IQR 4.0–13.7). Only one patient had an ipsilateral recurrence with the finding of an infiltrating carcinoma in the same breast after 14 years. The 5-year disease-free survival was 92.9%.

Conclusion

Pure DCIS in men is an extremely rare disease: proper diagnosis and management allow an excellent prognosis.

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography

Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years’ experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22−83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.