Survival outcomes of adjuvant treatment in upstaged clinical T2N0 rectal cancer: are we underutilizing therapy?

Background: Patients with rectal cancer staged as clinical T2N0 (cT2N0) are recommended to undergo upfront resection. However, when the tumor is subsequently upstaged to pathologic T3N0 (pT3N0), there are no clear guidelines for adjuvant treatment. This study aimed to analyze national trends in adjuvant management and to identify differences in morbidity or survival.

Current patterns of care and outcomes for dermatofibrosarcoma protuberans: An international multi-institutional collaborative.

Background: Dermatofibrosarcoma protuberans (DFSP) is a cutaneous sarcoma with an infiltrative growth pattern that makes it challenging to clear margins. High quality data regarding DFSP natural history, management, and outcomes are limited.

Methods: Data were retrospectively collected for adult DFSP patients who underwent resection at 10 institutions in eight countries.

National survey of patient perspectives on cost discussions among recipients of copay assistance.

Background: Individuals with cancer and other medical conditions often experience financial concerns from high costs-of-care and may utilize copay assistance programs (CAP). We sought to describe CAP recipients' experiences/preferences for cost discussions with clinicians.

Methods: We conducted a national, cross-sectional electronic-survey from 10/2022 to 11/2022 of CAP recipients with cancer or autoimmune conditions to assess patient perspectives on cost discussions.

Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.

Purpose: Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer.

Methods: We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice.

Results: Among 12,350 patients, 5,870 (47.