Best Practices for Delivering Neoadjuvant Therapy in Pancreatic Ductal Adenocarcinoma.

Importance: Neoadjuvant therapy (NT) is an increasingly used treatment strategy for patients with localized pancreatic ductal adenocarcinoma (PDAC). Little research has been conducted on cancer care delivery during NT, and the standards for optimal delivery of NT have not been defined.

Objective: To develop consensus best practices for delivering NT to patients with localized PDAC.

Radiation Therapy for Rectal Cancer: An ASTRO Clinical Practice Guideline Focused Update.

Purpose: With the results of several recently published clinical trials, this guideline focused update provides evidence-based recommendations for the indications and dose-fractionation regimens for neoadjuvant radiation therapy (RT), optimal sequencing of RT and systemic therapy in the context of total neoadjuvant therapy (TNT), and considerations for selective omission of RT and surgery for rectal cancer.

Methods: The American Society for Radiation Oncology convened a multidisciplinary task force to update 3 key questions that focused on the role of RT for patients with operable rectal cancer. The key questions addressed (1) indications for neoadjuvant RT, (2) selection of neoadjuvant regimens, and (3) indications for consideration of a nonoperative management (NOM) or local excision approach after definitive/preoperative chemoradiation.

Point/Counterpoint #1: Chemotherapy Alone Is a Sufficient Preoperative Treatment for Rectal cancer.

For decades, the standard neoadjuvant therapy regimen for locally advanced rectal cancer consisted of chemoradiation, surgical resection, and consideration of adjuvant systemic therapy. Additional data have emerged suggesting neoadjuvant systemic therapy as a reasonable alternative to chemoradiation in selected patients. In addition, a total neoadjuvant therapy approach integrating both chemotherapy and chemoradiation results in superior cancer outcomes and the potential for consideration of nonoperative management in patients with a clinical complete response.

Health Care Cost Reductions with Machine Learning-Directed Evaluations during Radiation Therapy - An Economic Analysis of a Randomized Controlled Study.

Background: Machine learning (ML) may cost-effectively direct health care by identifying patients most likely to benefit from preventative interventions to avoid negative and expensive outcomes. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT; NCT04277650) was a single-institution, randomized controlled study in which electronic health record-based ML accurately identified patients at high risk for acute care (emergency visit or hospitalization) during radiotherapy (RT) and targeted them for supplemental clinical evaluations. This ML-directed intervention resulted in decreased acute care utilization.

The Next Chapter in Immunotherapy and Radiation Combination Therapy: Cancer-Specific Perspectives.

Immunotherapeutic agents have revolutionized cancer treatment over the past decade. However, most patients fail to respond to immunotherapy alone. A growing body of preclinical studies highlights the potential for synergy between radiation therapy and immunotherapy, but the outcomes of clinical studies have been mixed.