Local Therapy for Metastatic Colorectal Cancer: A Case-Based Review.

ABSTRACT: The oligometastatic disease state, defined as a cancer with 5 or fewer sites of metastasis, is a therapeutic opportunity to improve oncologic outcomes. Colorectal cancer (CRC) was among the first for which oligometastatic treatment was used in routine clinical practice, and recent studies have shown potential for improved overall survival with metastasis-directed therapies. As CRC is the third most common cause of cancer death in men and women, improving oncologic outcomes in this population is of paramount importance. The relatively recent identification of this treatment paradigm and paucity of high-quality data have led to heterogeneity in clinical practice. This review will explore perspectives of a panel of surgical and radiation oncologists for complex or controversial cases of metastatic CRC.

Variation in Androgen Deprivation Therapy Use Among Men With Intermediate-Risk Prostate Cancer: Results From a Statewide Radiation Oncology Quality Consortium.

PURPOSE: For men with intermediate-risk prostate cancer treated with definitive therapy, the addition of androgen deprivation therapy (ADT) reduces the risk of distant metastasis and cancer-related mortality. However, the absolute benefit of ADT varies by baseline cancer risk. Estimates of prognosis have improved over time, and little is known about ADT decision making in the modern era. We sought to characterize variability and identify factors associated with intended ADT use within the Michigan Radiation Oncology Quality Consoritum (MROQC). MATERIALS AND METHODS: Patients with localized prostate cancer undergoing definitive radiation therapy were enrolled from June 9, 2020, to June 26, 2023 (n = 815). Prospective data were collected using standardized patient, physician, and physicist forms. Intended ADT use was prospectively defined and was the primary outcome. Associations with patient, tumor, and practice-related factors were tested with multivariable analyses. Random intercept modeling was used to estimate facility-level variability. RESULTS: Five hundred seventy patients across 26 facilities were enrolled with intermediate-risk disease. ADT was intended for 46% of men (n = 262/570), which differed by National Comprehensive Cancer Network favorable intermediate-risk (23.5%, n = 38/172) versus unfavorable intermediate-risk disease (56.3%, n = 224/398; P < .001). After adjusting for the statewide case mix, the predicted probability of intended ADT use varied significantly across facilities, ranging from 15.4% (95% CI, 5.4%-37.0%) to 71.7% (95% CI, 57.0%-82.9%), with P < .01. Multivariable analyses showed that grade group 3 (OR, 4.60 [3.20-6.67]), ≥50% positive cores (OR, 2.15 [1.43-3.25]), and prostate-specific antigen 10 to 20 (OR, 1.87 [1.24-2.84]) were associated with ADT use. Area under the curve was improved when incorporating MRI adverse features (0.76) or radiation treatment variables (0.76), but there remained significant facility-level heterogeneity in all models evaluated (P < .05). CONCLUSIONS: Within a state-wide consortium, there is substantial facility-level heterogeneity in intended ADT use for men with intermediate-risk prostate cancer. Future efforts are necessary to identify patients who will benefit most from ADT and to develop strategies to standardize appropriate use.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods: We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results: AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions: Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.

Utilization and predictors of postmastectomy radiation receipt in an Oncology Center in Zimbabwe.

PURPOSE: Few sub-Saharan African studies have ascertained utilization for postmastectomy radiation (PMRT) for breast cancer, the second most common cancer among African women. We estimated PMRT utilization and identified predictors of PMRT receipt in Zimbabwe. METHODS: Retrospective patient cohort included non-metastatic breast cancer patients treated from 2014 to 2019. PMRT eligibility was assigned per NCCN guidelines. Patients receiving chemotherapy for non-metastatic disease were also included. The primary endpoint was receipt of PMRT, defined as chest wall with/without regional nodal radiation. Predictors of receiving PMRT were identified using logistic regression. Model performance was evaluated using the c statistic and Hosmer-Lemeshow test for goodness-of-fit. RESULTS: 201 women with localized disease and median follow-up of 11.4 months (IQR 3.3-17.9) were analyzed. PMRT was indicated in 177 women and utilized in 59(33.3%). Insurance coverage, clinical nodal involvement, higher grade, positive margins, and hormone therapy receipt were associated with higher odds of PMRT receipt. In adjusted models, no hormone therapy (aOR 0.12, 95% CI 0.043, 0.35) and missing grade (aOR 0.07, 95% CI 0.01, 0.38) were associated with lower odds of PMRT receipt. The resulting c statistic was 0.84, with Hosmer-Lemeshow p-value of 0.93 indicating good model fit. CONCLUSION: PMRT was utilized in 33% of those meeting NCCN criteria. Missing grade and no endocrine therapy receipt were associated with reduced likelihood of PMRT utilization. In addition to practice adjustments such as increasing hypofractionation and increasing patient access to standard oncologic testing at diagnosis could increase postmastectomy utilization.

A Call for One Health in Medical Education: How the COVID-19 Pandemic Underscores the Need to Integrate Human, Animal, and Environmental Health.

During the early stages of the COVID-19 pandemic in 2020, the first author, then a fourth-year student at Harvard Medical School, was enrolled in a One Health clinical experience at Zoo New England where he was introduced to a transdisciplinary approach to integrate human, animal, and ecosystem health. Seeing the vast impact of the pandemic and knowing its roots as a zoonotic disease, he realized this approach was critical to his medical education and for preparation against future novel infectious diseases. Zoonotic diseases have been emerging into human populations with increasing frequency, leading to public health emergencies such as Ebola, avian influenza, and SARS. The SARS-CoV-2 narrative, starting in bats and then mutating through an intermediate host into humans, is another striking example of the interconnectedness between human, animal, and ecosystem health that underlies these infections. Preventing future pandemics will require a transdisciplinary One Health approach, and physicians should be prepared to participate in these discussions while advocating for One Health initiatives for the benefit of their current and future patients. Integration of One Health education into medical school curricula will also prepare future physicians for other complex and urgently important health issues such as climate change, antimicrobial resistance, and the impact of biodiversity loss. As the consequences of the COVID-19 pandemic persist, education in One Health must become a priority; it is essential to break down the conventional disciplinary silos of human medicine, veterinary medicine, environmental health, public health, and the social sciences, so that future health crises can be prevented and mitigated collaboratively.