Treatment of Locally Recurrent Rectal Cancer: A Review.

ABSTRACT: Up to 10% of patients with locally advanced rectal cancer will experience locoregional recurrence. In the setting of prior surgery and often radiation and chemotherapy, these represent uniquely challenging cases. When feasible, surgical resection offers the best chance for oncologic control yet risks significant morbidity. Studies have consistently indicated that a negative surgical resection margin is the strongest predictor of oncologic outcomes. Chemoradiation is often recommended to increase the chance of an R0 resection, and in cases of close/positive margins, intraoperative radiation/brachytherapy can be utilized. In patients who are not surgical candidates, radiation can provide symptomatic relief. Ongoing phase III trials are aiming to address questions regarding the role of reirradiation and induction multiagent chemotherapy regimens in this population.

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.

The Current Role of Human Papillomavirus Circulating Tumor DNA in Oropharynx Cancer.

ABSTRACT: Human papillomavirus infection is currently implicated in the majority of oropharyngeal squamous cell carcinoma cases diagnosed in the United States. Circulating tumor DNA (ctDNA) has emerged as a potential biomarker for human papillomavirus-related oropharyngeal squamous cell carcinoma and has the opportunity to improve the diagnosis, treatment, and surveillance of patients with this disease. Changes in ctDNA levels during and after primary therapy may be related to disease response, which can possibly have implications for treatment intensification or de-escalation strategies. Further, ctDNA seems to be sensitive and specific for disease recurrence and may improve upon current methods for assessing both treatment response and failure. In this review, we examine the relevant literature on the use of ctDNA for oropharyngeal cancer treatment and surveillance and discuss current limitations and future directions for this promising biomarker.

The Combined Effect of FGFR Inhibition and PD-1 Blockade Promotes Tumor-Intrinsic Induction of Antitumor Immunity.

The success of targeted or immune therapies is often hampered by the emergence of resistance and/or clinical benefit in only a subset of patients. We hypothesized that combining targeted therapy with immune modulation would show enhanced antitumor responses. Here, we explored the combination potential of erdafitinib, a fibroblast growth factor receptor (FGFR) inhibitor under clinical development, with PD-1 blockade in an autochthonous FGFR2K660N/p53mut lung cancer mouse model. Erdafitinib monotherapy treatment resulted in substantial tumor control but no significant survival benefit. Although anti-PD-1 alone was ineffective, the erdafitinib and anti-PD-1 combination induced significant tumor regression and improved survival. For both erdafitinib monotherapy and combination treatments, tumor control was accompanied by tumor-intrinsic, FGFR pathway inhibition, increased T-cell infiltration, decreased regulatory T cells, and downregulation of PD-L1 expression on tumor cells. These effects were not observed in a KRASG12C-mutant genetically engineered mouse model, which is insensitive to FGFR inhibition, indicating that the immune changes mediated by erdafitinib may be initiated as a consequence of tumor cell killing. A decreased fraction of tumor-associated macrophages also occurred but only in combination-treated tumors. Treatment with erdafitinib decreased T-cell receptor (TCR) clonality, reflecting a broadening of the TCR repertoire induced by tumor cell death, whereas combination with anti-PD-1 led to increased TCR clonality, suggesting a more focused antitumor T-cell response. Our results showed that the combination of erdafitinib and anti-PD-1 drives expansion of T-cell clones and immunologic changes in the tumor microenvironment to support enhanced antitumor immunity and survival.