Contralateral breast cancer after curative-intent treatment for ductal carcinoma in situ: Rate and associated clinicopathological and imaging risk factors

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.

Ipsilateral tumor recurrence risk in women with ductal carcinoma in situ: application of the Van Nuys Prognostic Index and the Memorial Sloan Kettering Cancer Center nomogram

PURPOSE

To apply the Van Nuys Prognostic Index (VNPI) and the Memorial Sloan Kettering Cancer Center (MSKCC) ductal carcinoma in situ (DCIS) nomogram to DCIS patients with known long-term outcomes.

METHODS

A retrospective review was performed of consecutive patients diagnosed with DCIS from 2007 to 2014. Included patients underwent breast-conserving surgery (BCS) and were followed with imaging for at least five years. For each patient, the VNPI and MSKCC nomogram risk estimates were determined. In addition, variables used in both models were compared between women with and without recurrences using the Wilcoxon signed-rank test and the Pearson's chi-squared test.

RESULTS

Over the eight-year period, 456 women (average age 57 years, range 30-87) underwent BCS for DCIS. Thirty-one (6.8%) experienced an ipsilateral recurrence. The average VNPI scores were 7 (range 5-9) and 7 (range 4-10) for women with and without a recurrence (p = 0.14), respectively, with 4-6, 7-9, and 10-12 being the low, moderate, and high-risk groups, respectively. Per the MSKCC nomogram, the average five-year recurrence risks were 5% (range 1-12%) and 4% (range 1-38%) for women with and without a recurrence (p = 0.09), respectively. The recurrence risk-related variables were younger patient age, need for one or more re-excision surgeries, and use of endocrine therapy for 0 to less than five years after surgery.

CONCLUSION

Ipsilateral tumor recurrence risk estimates based on the VNPI and MSKCC nomogram are similar between women with DCIS who did and did not have a recurrence, suggesting that more robust prognostic models are needed.

AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2022

Introduction

The AGO (Arbeitsgemeinschaft Gynäkologische Onkologie, German Gynecological Oncology Group) Task Force on Diagnosis and Treatment of Breast Cancer as an interdisciplinary team consists of specialists from gynecological oncology, pathology, diagnostic radiology, medical oncology, and radiation oncology with a special focus on breast cancer.

Methods

The updated evidence-based treatment recommendation 2022 for early breast cancer (EBC) and metastatic breast cancer of the AGO Task Force has been released.

Results and Conclusion

This paper captures the update of EBC.