Testing and Validation of a Custom Retrained Large Language Model for the Supportive Care of HN Patients with External Knowledge Base.

PURPOSE: This study aimed to develop a retrained large language model (LLM) tailored to the needs of HN cancer patients treated with radiotherapy, with emphasis on symptom management and survivorship care. METHODS: A comprehensive external database was curated for training ChatGPT-4, integrating expert-identified consensus guidelines on supportive care for HN patients and correspondences from physicians and nurses within our institution's electronic medical records for 90 HN patients. The performance of our model was evaluated using 20 patient post-treatment inquiries that were then assessed by three Board certified radiation oncologists (RadOncs). The rating of the model was assessed on a scale of 1 (strongly disagree) to 5 (strongly agree) based on accuracy, clarity of response, completeness s, and relevance. RESULTS: The average scores for the 20 tested questions were 4.25 for accuracy, 4.35 for clarity, 4.22 for completeness, and 4.32 for relevance, on a 5-point scale. Overall, 91.67% (220 out of 240) of assessments received scores of 3 or higher, and 83.33% (200 out of 240) received scores of 4 or higher. CONCLUSION: The custom-trained model demonstrates high accuracy in providing support to HN patients offering evidence-based information and guidance on their symptom management and survivorship care.

Elective Irradiation of Retropharyngeal Lymph Nodes as an Indication for Adjuvant Radiation Therapy After Transoral Surgery for Tonsil Cancer.

Treatment of squamous cell carcinoma of the tonsil involves primary radiation therapy (RT) or surgical resection. Historically, if RT was the primary or adjuvant treatment modality, most of the bilateral retropharyngeal lymph nodes (RPLNs) were treated electively with a therapeutic dose for subclinical disease, regardless of whether radiographically pathologic lymph nodes were seen on initial diagnostic imaging. De-escalation strategies include the incorporation of transoral surgery with the goal to either eliminate or reduce the dose of adjuvant RT or chemotherapy. Transoral surgery does not include elective removal of the RPLNs, and no guideline or outcome paper recommends adjuvant RT specifically to electively treat RPLNs. In this Topic Discussion, we discuss pertinent literature and suggest management decisions. The management decisions discussed in this Topic Discussion pertain to only tonsillar primaries and not those of the soft palate or base of the tongue.

Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy.

Introduction: Manual review of organ at risk (OAR) contours is crucial for creating safe radiotherapy plans but can be time-consuming and error prone. Statistical and deep learning models show the potential to automatically detect improper contours by identifying outliers using large sets of acceptable data (knowledge-based outlier detection) and may be able to assist human reviewers during review of OAR contours. Methods: This study developed an automated knowledge-based outlier detection method and assessed its ability to detect erroneous contours for all common head and neck (HN) OAR types used clinically at our institution. We utilized 490 accurate CT-based HN structure sets from unique patients, each with forty-two HN OAR contours when anatomically present. The structure sets were distributed as 80% for training, 10% for validation, and 10% for testing. In addition, 190 and 37 simulated contours containing errors were added to the validation and test sets, respectively. Single-contour features, including location, shape, orientation, volume, and CT number, were used to train three single-contour feature models (z-score, Mahalanobis distance [MD], and autoencoder [AE]). Additionally, a novel contour-to-contour relationship (CCR) model was trained using the minimum distance and volumetric overlap between pairs of OAR contours to quantify overlap and separation. Inferences from single-contour feature models were combined with the CCR model inferences and inferences evaluating the number of disconnected parts in a single contour and then compared. Results: In the test dataset, before combination with the CCR model, the area under the curve values were 0.922/0.939/0.939 for the z-score, MD, and AE models respectively for all contours. After combination with CCR model inferences, the z-score, MD, and AE had sensitivities of 0.838/0.892/0.865, specificities of 0.922/0.907/0.887, and balanced accuracies (BA) of 0.880/0.900/0.876 respectively. In the validation dataset, with similar overall performance and no signs of overfitting, model performance for individual OAR types was assessed. The combined AE model demonstrated minimum, median, and maximum BAs of 0.729, 0.908, and 0.980 across OAR types. Discussion: Our novel knowledge-based method combines models utilizing single-contour and CCR features to effectively detect erroneous OAR contours across a comprehensive set of 42 clinically used OAR types for HN radiotherapy.

Implementation of the REFLECT Communication Curriculum for Clinical Oncology Graduate Medical Education.

Background: Communication and interpersonal skills are essential medical components of oncology patient care. Patients and families rely on physicians for treatment, expertise, guidance, hope, meaning, and compassion throughout a life-threatening illness. A provider's inability to empathize with patients is linked to physician-related fatigue and burnout. Because oncology training programs focus on teaching evidence-based medicine and clinical acumen, little time may be dedicated to professional development and acquisition of interactive skills. Traditional communication courses typically include two components: formal, knowledge-based learning skills, which are gained from didactic lectures, and role-playing, which usually occurs in small groups. We report the implementation of a novel longitudinal communication curriculum for trainees in Oncology. Materials and Methods: At a single-center institution, an innovative communication curriculum titled "REFLECT" (Respect, Empathy, Facilitate Effective Communication, Listen, Elicit Information, Compassion, and Teach Others) was implemented for radiation oncology residents and medical oncology fellows to improve and refine physician/patient interactions. All oncology specialty residents and fellows were eligible to participate in this communication curriculum. The curriculum emphasized a reflective process to guide trainees through challenging scenarios. Results: Since October 2018, this comprehensive course consisted of quarterly (four hour) workshops comprising assigned reading, knowledge assessments, didactic lectures, expert guest lecturers, standardized patient simulations, role-playing, patient/expert panels, coaching, reflective writing, and debriefing/feedback sessions. The curriculum provided longitudinal communication training integrated with the learners' daily physician/patient encounters rather than occasional isolated experiences. Fifteen workshops have been completed. Each focused on navigating challenging situations with patients, loved ones, or colleagues. Conclusions: Future directions of the curriculum will entail improving the communication skills of oncology trainees and gathering communication improvement data to assess the program's success formally.

Mean Oral Cavity Organ-at-Risk Dose Predicts Opioid Use and Hospitalization during Radiotherapy for Patients with Head and Neck Tumors.

BACKGROUND: Approximately 75% of all head and neck cancer patients are treated with radiotherapy (RT). RT to the oral cavity results in acute and late adverse events which can be severe and detrimental to a patient's quality of life and function. The purpose of this study was to explore associations between RT dose to a defined oral cavity organ-at-risk (OAR) avoidance structure, provider- and patient-reported outcomes (PROs), opioid use, and hospitalization. METHODS: This was a retrospective analysis of prospectively obtained outcomes using multivariable modeling. The study included 196 patients treated with RT involving the oral cavity for a head and neck tumor. A defined oral cavity OAR avoidance structure was used in all patients for RT treatment planning. Validated PROs were collected prospectively. Opioid use and hospitalization were abstracted electronically from medical records. RESULTS: Multivariable modeling revealed the mean dose to the oral cavity OAR was significantly associated with opioid use (p = 0.0082) and hospitalization (p = 0.0356) during and within 30 days of completing RT. CONCLUSIONS: The findings of this study may be valuable in RT treatment planning for patients with tumors of the head and neck region to reduce the need for opioid use and hospitalization during treatment.