Predictive [18F]-FDG PET/CT-Based Radiogenomics Modelling of Driver Gene Mutations in Non-small Cell Lung Cancer.

RATIONALE AND OBJECTIVES: To investigate whether [18F]-FDG PET/CT-derived radiomics may correlate with driver gene mutations in non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: In this IRB-approved retrospective study, 203 patients with surgically treated NSCLC who underwent subsequent genomic analysis of the primary tumour at our institution between December 2004 and January 2014 were identified. Of those, 128 patients (mean age 62.4 ± 10.8 years; range: 35-84) received preoperative [18F]-FDG PET/CT as part of their initial staging and thus were included in the study. PET and CT image segmentation and feature extraction were performed semi-automatically with an open-source software platform (LIFEx, Version 6.30, lifexsoft.org). Molecular profiles using different next-generation sequencing (NGS) panels were collected from a web-based resource (cBioPortal.ca for Cancer genomics). Two statistical models were then built to evaluate the predictive ability of [18F]-FDG PET/CT-derived radiomics features for driver gene mutations in NSCLC. RESULTS: More than half (68/128, 53%) of all tumour samples harboured three or more gene mutations. Overall, 55% of tumour samples demonstrated a mutation in TP53, 26% of samples had alterations in KRAS and 17% in EGFR. Extensive statistical analysis resulted in moderate to good predictive ability. The highest Youden Index for TP53 was achieved using combined PET/CT features (0.70), for KRAS using PET features only (0.57) and for EGFR using CT features only (0.60). CONCLUSION: Our study demonstrated a moderate to good correlation between radiomics features and driver gene mutations in NSCLC, indicating increased predictive ability of genomic profiles using combined [18F]-FDG PET/CT-derived radiomics features.

Radiology-Pathology Concordance and Prognostication of Nodal Features in pN+ Oral Cavity Cancer.

BACKGROUND AND PURPOSE: The aims of our study are to evaluate the diagnostic performance and prognostic value of radiological lymph node (LN) characteristics in pN+ oral cavity squamous carcinoma (OSCC). MATERIALS AND METHODS: pN+ OSCC treated between 2012 and 2020 were included. Preoperative imaging was reviewed by a single radiologist blinded to pathologic findings for the following nodal features: imaging-positive LN (iN+), laterality and total number, and image-identified extranodal extension (iENE). The sensitivity of iN+ for pN+ was calculated. The diagnostic performance of other nodal features was evaluated in the iN+ subgroup. The association of radiologic nodal features with overall survival (OS) was evaluated. Inter-rater kappa for radiologic nodal features was assessed in 100 randomly selected cases. RESULTS: Of 406 pN+ OSCC, 288 were iN+. The sensitivity of iN+ for pN+ was 71% overall, and improved to 89% for pN+ LN >1.5 cm. Within iN+, sensitivity/specificity for LN size (>3 cm), total LN number (>4), and ENE were 0.44/0.95, 0.57/0.84, and 0.27/0.96, respectively. Sensitivity of iENE was higher in the subset, with major (>2 mm) versus minor (≤2 mm) pENE (43% vs. 13%, p = 0.001). Reduced OS was observed in iN+ versus iN- (p = 0.006), iENE+ versus iENE- (p = 0.004), LN size >3 versus ≤3 cm (p < 0.001), and higher LN number (p < 0.001). Inter-rater kappa for iN+, laterality, total LN number, and presence of iENE were 0.71, 0.57, 0.78, and 0.69, respectively. CONCLUSION: Our study shows that despite modest sensitivity of most radiological nodal features, the specificity of image-identified nodal features is high and their prognostic values are retained in pN+ OSCC. LEVEL OF EVIDENCE: Level 3 (retrospective review comparing cases and controls) Laryngoscope, 2024.

The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study.

We aimed to investigate whether [18F]F-FDG-PET/CT-derived radiomics can classify histologic subtypes and determine the anatomical origin of various malignancies. In this IRB-approved retrospective study, 391 patients (age = 66.7 ± 11.2) with pulmonary (n = 142), gastroesophageal (n = 128) and head and neck (n = 121) malignancies were included. Image segmentation and feature extraction were performed semi-automatically. Two models (all possible subset regression [APS] and recursive partitioning) were employed to predict histology (squamous cell carcinoma [SCC; n = 219] vs. adenocarcinoma [AC; n = 172]), the anatomical origin, and histology plus anatomical origin. The recursive partitioning algorithm outperformed APS to determine histology (sensitivity 0.90 vs. 0.73; specificity 0.77 vs. 0.65). The recursive partitioning algorithm also revealed good predictive ability regarding anatomical origin. Particularly, pulmonary malignancies were identified with high accuracy (sensitivity 0.93; specificity 0.98). Finally, a model for the synchronous prediction of histology and anatomical disease origin resulted in high accuracy in determining gastroesophageal AC (sensitivity 0.88; specificity 0.92), pulmonary AC (sensitivity 0.89; specificity 0.88) and head and neck SCC (sensitivity 0.91; specificity 0.92). Adding PET-features was associated with marginal incremental value for both the prediction of histology and origin in the APS model. Overall, our study demonstrated a good predictive ability to determine patients' histology and anatomical origin using [18F]F-FDG-PET/CT-derived radiomics features, mainly from CT.

Development and Standardization of an Osteoradionecrosis Classification System in Head and Neck Cancer: Implementation of a Risk-Based Model.

PURPOSE: Osteoradionecrosis of the jaw (ORN) can manifest in varying severity. The aim of this study is to identify ORN risk factors and develop a novel classification to depict the severity of ORN. METHODS: Consecutive patients with head and neck cancer (HNC) treated with curative-intent intensity-modulated radiation therapy (IMRT) (≥45 Gy) from 2011 to 2017 were included. Occurrence of ORN was identified from in-house prospective dental and clinical databases and charts. Multivariable logistic regression model was used to identify risk factors and stratify patients into high-risk and low-risk groups. A novel ORN classification system was developed to depict ORN severity by modifying existing systems and incorporating expert opinion. The performance of the novel system was compared with 15 existing systems for their ability to identify and predict serious ORN event (jaw fracture or requiring jaw resection). RESULTS: ORN was identified in 219 of 2,732 (8%) consecutive patients with HNC. Factors associated with high risk of ORN were oral cavity or oropharyngeal primaries, received IMRT dose ≥60 Gy, current/ex-smokers, and/or stage III to IV periodontal condition. The ORN rate for high-risk versus low-risk patients was 12.7% versus 3.1% (P < .001) with an AUC of 0.71. Existing ORN systems overclassified serious ORN events and failed to recognize maxillary ORN. A novel ORN classification system, ClinRad, was proposed on the basis of vertical extent of bone necrosis and presence/absence of exposed bone/fistula. This system detected serious ORN events in 5.7% of patients and statistically outperformed existing systems. CONCLUSION: We identified risk factors for ORN and proposed a novel ORN classification system on the basis of vertical extent of bone necrosis and presence/absence of exposed bone/fistula. It outperformed existing systems in depicting the seriousness of ORN and may facilitate clinical care and clinical trials.

Prospective manipulation of the gut microbiome with microbial ecosystem therapeutic 4 (MET4) in HPV-related locoregionally-advanced oropharyngeal cancer squamous cell carcinoma (LA-OPSCC) undergoing primary chemoradiation: ROMA2 study.

BACKGROUND: Gut microbiome modulation to boost antitumor immune responses is under investigation. METHODS: ROMA-2 evaluated the microbial ecosystem therapeutic (MET)-4 oral consortia, a mixture of cultured human stool-derived immune-responsiveness associated bacteria, given with chemoradiation (CRT) in HPV-related oropharyngeal cancer patients. Co-primary endpoints were safety and changes in stool cumulative MET-4 taxa relative abundance (RA) by 16SRNA sequencing. Stools and plasma were collected pre/post-MET-4 intervention for microbiome and metabolome analysis. RESULTS: Twenty-nine patients received ≥1 dose of MET-4 and were evaluable for safety: drug-related adverse events (AEs) occurred in 13/29 patients: all grade 1-2 except one grade 3 (diarrhea). MET-4 was discontinued early in 7/29 patients due to CRT-induced toxicity, and in 1/29 due to MET-4 AEs. Twenty patients were evaluable for ecological endpoints: there was no increase in stool MET-4 RA post-intervention but trended to increase in stage III patients (p = 0.06). MET-4 RA was higher in stage III vs I-II patients at week 4 (p = 0.03) and 2-month follow-up (p = 0.01), which correlated with changes in plasma and stool targeted metabolomics. CONCLUSIONS: ROMA-2 did not meet its primary ecologic endpoint, as no engraftment was observed in the overall cohort. Exploratory findings of engraftment in stage III patients warrants further investigation of microbiome interventions in this subgroup.

Physician and Artificial Intelligence Chatbot Responses to Cancer Questions From Social Media.

Importance: Artificial intelligence (AI) chatbots pose the opportunity to draft template responses to patient questions. However, the ability of chatbots to generate responses based on domain-specific knowledge of cancer remains to be tested. Objective: To evaluate the competency of AI chatbots (GPT-3.5 [chatbot 1], GPT-4 [chatbot 2], and Claude AI [chatbot 3]) to generate high-quality, empathetic, and readable responses to patient questions about cancer. Design, Setting, and Participants: This equivalence study compared the AI chatbot responses and responses by 6 verified oncologists to 200 patient questions about cancer from a public online forum. Data were collected on May 31, 2023. Exposures: Random sample of 200 patient questions related to cancer from a public online forum (Reddit r/AskDocs) spanning from January 1, 2018, to May 31, 2023, was posed to 3 AI chatbots. Main Outcomes and Measures: The primary outcomes were pilot ratings of the quality, empathy, and readability on a Likert scale from 1 (very poor) to 5 (very good). Two teams of attending oncology specialists evaluated each response based on pilot measures of quality, empathy, and readability in triplicate. The secondary outcome was readability assessed using Flesch-Kincaid Grade Level. Results: Responses to 200 questions generated by chatbot 3, the best-performing AI chatbot, were rated consistently higher in overall measures of quality (mean, 3.56 [95% CI, 3.48-3.63] vs 3.00 [95% CI, 2.91-3.09]; P < .001), empathy (mean, 3.62 [95% CI, 3.53-3.70] vs 2.43 [95% CI, 2.32-2.53]; P < .001), and readability (mean, 3.79 [95% CI, 3.72-3.87] vs 3.07 [95% CI, 3.00-3.15]; P < .001) compared with physician responses. The mean Flesch-Kincaid Grade Level of physician responses (mean, 10.11 [95% CI, 9.21-11.03]) was not significantly different from chatbot 3 responses (mean, 10.31 [95% CI, 9.89-10.72]; P > .99) but was lower than those from chatbot 1 (mean, 12.33 [95% CI, 11.84-12.83]; P < .001) and chatbot 2 (mean, 11.32 [95% CI, 11.05-11.79]; P = .01). Conclusions and Relevance: The findings of this study suggest that chatbots can generate quality, empathetic, and readable responses to patient questions comparable to physician responses sourced from an online forum. Further research is required to assess the scope, process integration, and patient and physician outcomes of chatbot-facilitated interactions.

Shortcut learning in medical AI hinders generalization: method for estimating AI model generalization without external data.

Healthcare datasets are becoming larger and more complex, necessitating the development of accurate and generalizable AI models for medical applications. Unstructured datasets, including medical imaging, electrocardiograms, and natural language data, are gaining attention with advancements in deep convolutional neural networks and large language models. However, estimating the generalizability of these models to new healthcare settings without extensive validation on external data remains challenging. In experiments across 13 datasets including X-rays, CTs, ECGs, clinical discharge summaries, and lung auscultation data, our results demonstrate that model performance is frequently overestimated by up to 20% on average due to shortcut learning of hidden data acquisition biases (DAB). Shortcut learning refers to a phenomenon in which an AI model learns to solve a task based on spurious correlations present in the data as opposed to features directly related to the task itself. We propose an open source, bias-corrected external accuracy estimate, PEst, that better estimates external accuracy to within 4% on average by measuring and calibrating for DAB-induced shortcut learning.

Telemedicine-enabled biofeedback electropalatography rehabilitation (TEBER): A pilot study for patients treated with surgery for oral cavity carcinoma.

BACKGROUND: To address the rehabilitative barriers to frequency and precision of care, we conducted a pilot study of a biofeedback electropalatography (EPG) device paired with telemedicine for patients who underwent primary surgery +/- adjuvant radiation for oral cavity carcinoma. We hypothesized that lingual optimization followed by telemedicine-enabled biofeedback electropalatography rehabilitation (TEBER) would further improve speech and swallowing outcomes after "standard-of-care" SOC rehabilitation. METHOD: Pilot prospective 8-week (TEBER) program following 8 weeks of (SOC) rehabilitation. RESULTS: Twenty-seven patients were included and 11 completed the protocol. When examining the benefit of TEBER independent of standard of care, "range-of-liquids" improved by +0.36 [95% CI, 0.02-0.70, p = 0.05] and "range-of-solids" improved by +0.73 [95% CI, 0.12-1.34, p = 0.03]. There was a positive trend toward better oral cavity obliteration; residual volume decreased by -1.2 [95% CI, -2.45 to 0.053, p = 0.06], and "nutritional-mode" increased by +0.55 [95% CI, -0.15 to 1.24, p = 0.08]. CONCLUSION: This pilot suggests that TEBER bolsters oral rehabilitation after 8 weeks of SOC lingual range of motion.

Exploring the Language Used to Describe Older Patients at Multidisciplinary Cancer Conferences.

Older adults with cancer often present with distinct complexities that complicate their care, yet the language used to discuss their management at multidisciplinary cancer conferences (MCCs) remains poorly understood. A mixed methods study was conducted at a tertiary cancer centre in Toronto, Canada, where MCCs spanning five tumour sites were attended over six months. For presentations pertaining to a patient aged 75 or older, a standardized data collection form was used to record their demographic, cancer-related, and non-cancer-related information, as well as the presenter's specialty and training level. Descriptive statistics and thematic analysis were employed to explore MCC depictions of older patients (n = 75). Frailty status was explicitly mentioned in 20.0% of presentations, but discussions more frequently referenced comorbidity burden (50.7%), age (33.3%), and projected treatment tolerance (30.7%) as surrogate measures. None of the presentations included mentions of formal geriatric assessment (GA) or validated frailty tools; instead, presenters tended to feature select GA domains and subjective descriptions of appearance ("looks to be fit") or overall health ("relatively healthy"). In general, MCCs appeared to rely on age-focused language that may perpetuate ageism. Further work is needed to investigate how frailty and geriatric considerations can be objectively incorporated into discussions in geriatric oncology.

Stereotactic Radiation for Ultra-Central Non-Small Cell Lung Cancer: A Safety and Efficacy Trial (SUNSET).

PURPOSE: The use of stereotactic body radiation therapy for tumors in close proximity to the central mediastinal structures has been associated with a high risk of toxicity. This study (NCT03306680) aimed to determine the maximally tolerated dose of stereotactic body radiation therapy for ultracentral non-small cell lung carcinoma, using a time-to-event continual reassessment methodology. METHODS AND MATERIALS: Patients with T1-3N0M0 (≤6 cm) non-small cell lung carcinoma were eligible. The maximally tolerated dose was defined as the dose of radiation therapy associated with a ≤30% rate of grade (G) 3 to 5 prespecified treatment-related toxicity occurring within 2 years of treatment. The starting dose level was 60 Gy in 8 daily fractions. The dose-maximum hotspot was limited to 120% and within the planning tumor volume; tumors with endobronchial invasion were excluded. This primary analysis occurred 2 years after completion of accrual. RESULTS: Between March 2018 and April 2021, 30 patients were enrolled at 5 institutions. The median age was 73 years (range, 65-87) and 17 (57%) were female. Planning tumor volume was abutting proximal bronchial tree in 19 (63%), esophagus 5 (17%), pulmonary vein 1 (3.3%), and pulmonary artery 14 (47%). All patients received 60 Gy in 8 fractions. The median follow-up was 37 months (range, 8.9-51). Two patients (6.7%) experienced G3-5 adverse events related to treatment: 1 patient with G3 dyspnea and 1 G5 pneumonia. The latter had computed tomography findings consistent with a background of interstitial lung disease. Three-year overall survival was 72.5% (95% CI, 52.3%-85.3%), progression-free survival 66.1% (95% CI, 46.1%-80.2%), local control 89.6% (95% CI, 71.2%-96.5%), regional control 96.4% (95% CI, 77.2%-99.5%), and distant control 85.9% (95% CI, 66.7%-94.5%). Quality-of-life scores declined numerically over time, but the decreases were not clinically or statistically significant. CONCLUSIONS: Sixty Gy in 8 fractions, planned and delivered with only a moderate hotspot, has a favorable adverse event rate within the prespecified acceptability criteria and results in excellent control for ultracentral tumors.

Modelled impact of Tiny Targets on the distribution and abundance of riverine tsetse.

BACKGROUND: The insecticide-treated baits known as Tiny Targets are one of the cheapest means of controlling riverine species of tsetse flies, the vectors of the trypanosomes that cause sleeping sickness in humans. Models of the efficacy of these targets deployed near rivers are potentially useful in planning control campaigns and highlighting the principles involved. METHODS AND PRINCIPAL FINDINGS: To evaluate the potential of models, we produced a simple non-seasonal model of the births, deaths, mobility and aging of tsetse, and we programmed it to simulate the impact of seven years of target use against the tsetse, Glossina fuscipes fuscipes, in the riverine habitats of NW Uganda. Particular attention was given to demonstrating that the model could explain three matters of interest: (i) good control can be achieved despite the degradation of targets, (ii) local elimination of tsetse is impossible if invasion sources are not tackled, and (iii) with invasion and target degradation it is difficult to detect any effect of control on the age structure of the tsetse population. CONCLUSIONS: Despite its simplifications, the model can assist planning and teaching, but allowance should be made for any complications due to seasonality and management challenges associated with greater scale.

Causes and impact of delays during the COVID-19 pandemic on head and neck cancer diagnosis.

BACKGROUND: The causes for delays during the COVID19 pandemic and their impact on head and neck cancer (HNC) diagnosis and staging are not well described. METHODS: Two cohorts were defined a priori for review and analysis-a Pre-Pandemic cohort (June 1 to December 31, 2019) and a Pandemic cohort (June 1 to December 31, 2020). Delays were categorized as COVID-19 related or not, and as clinician, patient, or policy related. RESULTS: A total of 638 HNC patients were identified including 327 in the Pre-Pandemic Cohort and 311 in the Pandemic Cohort. Patients in the Pandemic cohort had more N2-N3 category (41% vs. 33%, p = 0.03), T3-T4 category (63% vs. 50%, p = 0.002), and stage III-IV (71% vs. 58%, p < 0.001) disease. Several intervals in the diagnosis to treatment pathway were significantly longer in the pandemic cohort as compared to the Pre-Pandemic cohort. Among the pandemic cohort, 146 (47%) experienced a delay, with 112 related to the COVID-19 pandemic; 80 (71%) were clinician related, 15 (13%) were patient related, and 17 (15%) were policy related. CONCLUSIONS: Patients in the Pandemic cohort had higher stage disease at diagnosis and longer intervals along the diagnostic pathway, with COVID-19 related clinician factors being the most common cause of delay.

Stereotactic Radiation Therapy in Early Non-Small Cell Lung Cancer and Interstitial Lung Disease: A Nonrandomized Clinical Trial.

Importance: Patients with interstitial lung disease (ILD) and early-stage non-small cell lung cancer (NSCLC) have been reported to be at high risk of toxic effects after stereotactic ablative radiotherapy (SABR), but for many patients, there are limited alternative treatment options. Objective: To prospectively assess the benefits and toxic effects of SABR in this patient population. Design, Setting, and Participants: This prospective cohort study was conducted at 6 academic radiation oncology institutions, 5 in Canada and 1 in Scotland, with accrual between March 7, 2019, and January 12, 2022. Patients aged 18 years or older with fibrotic ILD and a diagnosis of T1-2N0 NSCLC who were not candidates for surgical resection were enrolled. Intervention: Patients were treated with SABR to a dose of 50 Gy in 5 fractions every other day. Main Outcomes and Measures: The study prespecified that SABR would be considered worthwhile if median overall survival-the primary end point-was longer than 1 year, with a grade 3 to 4 risk of toxic effects less than 35% and a grade 5 risk of toxic effects less than 15%. Secondary end points included toxic effects, progression-free survival (PFS), local control (LC), quality-of-life outcomes, and changes in pulmonary function. Intention-to-treat analysis was conducted. Results: Thirty-nine patients enrolled and received SABR. Median age was 78 (IQR, 67-83) years and 59% (n = 23) were male. At baseline, 70% (26 of 37) of patients reported dyspnea, median forced expiratory volume in first second of expiration was 80% (IQR, 66%-90%) predicted, median forced vital capacity was 84% (IQR, 69%-94%) predicted, and median diffusion capacity of the lung for carbon monoxide was 49% (IQR, 38%-61%) predicted. Median follow-up was 19 (IQR, 14-25) months. Overall survival at 1 year was 79% (95%, CI 62%-89%; P < .001 vs the unacceptable rate), and median overall survival was 25 months (95% CI, 14 months to not reached). Median PFS was 19 months (95% CI, 13-28 months), and 2-year LC was 92% (95% CI, 69%-98%). Adverse event rates (highest grade per patient) were grade 1 to 2: n = 12 (31%), grade 3: n = 4 (10%), grade 4: n = 0, and grade 5: n = 3 (7.7%, all due to respiratory deterioration). Conclusions and Relevance: In this trial, use of SABR in patients with fibrotic ILD met the prespecified acceptability thresholds for both toxicity and efficacy, supporting the use of SABR for curative-intent treatment after a careful discussion of risks and benefits. Trial Registration: ClinicalTrials.gov Identifier: NCT03485378.

RADCURE: An open-source head and neck cancer CT dataset for clinical radiation therapy insights.

PURPOSE: This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. ACQUISITION AND VALIDATION METHODS: RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ-at-risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom-built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system (Tumor-Node-Metastasis Classification System of Malignant Tumors). The median patient age is 63, with the final dataset including 80% males. Half of the cohort is diagnosed with oropharyngeal cancer, while laryngeal, nasopharyngeal, and hypopharyngeal cancers account for 25%, 12%, and 5% of cases, respectively. The median duration of follow-up is five years, with 60% of the cohort surviving until the last follow-up point. DATA FORMAT AND USAGE NOTES: The dataset provides images and contours in DICOM CT and RT-STRUCT formats, respectively. We have standardized the nomenclature for individual contours-such as the gross primary tumor, gross nodal volumes, and 19 organs-at-risk-to enhance the RT-STRUCT files' utility. Accompanying demographic, clinical, and treatment data are supplied in a comma-separated values (CSV) file format. This comprehensive dataset is publicly accessible via The Cancer Imaging Archive. POTENTIAL APPLICATIONS: RADCURE's amalgamation of imaging, clinical, demographic, and treatment data renders it an invaluable resource for a broad spectrum of radiomics image analysis research endeavors. Researchers can utilize this dataset to advance routine clinical procedures using machine learning or artificial intelligence, to identify new non-invasive biomarkers, or to forge prognostic models.

Multimodal detection of molecular residual disease in high-risk locally advanced squamous cell carcinoma of the head and neck.

Up to 30% of patients with locally advanced head and neck squamous cell carcinoma (LA-HNSCC) relapse. Molecular residual disease (MRD) detection using multiple assays after definitive therapy has not been reported. In this study, we included patients with LA-HNSCC (stage III Human Papilloma virus (HPV)-positive, III-IVB HPV-negative) treated with curative intent. Plasma was collected pre-treatment, at 4-6 weeks (FU1) and 8-12 weeks (FU2) post-treatment. Circulating tumor DNA (ctDNA) was analyzed using a tumor-informed (RaDaR®) and a tumor-naïve (CAPP-seq) assay. HPV DNA was measured using HPV-sequencing (HPV-seq) and digital PCR (dPCR). A total of 86 plasma samples from 32 patients were analyzed; all patients with at least 1 follow-up sample. Most patients were stage III HPV-positive (50%) and received chemoradiation (78%). No patients had radiological residual disease at FU2. With a median follow-up of 25 months, there were 7 clinical relapses. ctDNA at baseline was detected in 15/17 (88%) by RaDaR and was not associated with recurrence free survival (RFS). Two patients relapsed within a year after definitive therapy and showed MRD at FU2 using RaDaR; detection of ctDNA during follow-up was associated with shorter RFS (p < 0.001). ctDNA detection by CAPP-seq pre-treatment and during follow-up was not associated with RFS (p = 0.09). HPV DNA using HPV-seq or dPCR during follow-up was associated with shorter RFS (p < 0.001). Sensitivity and specificity for MRD at FU2 using RaDaR was 40% and 100% versus 20 and 90.5% using CAPP-seq. Sensitivity and specificity for MRD during follow-up using HPV-seq was 100% and 91.7% versus 50% and 100% using dPCR. In conclusion, HPV DNA and ctDNA can be detected in LA-HNSCC before definitive therapy. The RaDaR assay but not CAPP-seq may detect MRD in patients who relapse within 1 year. HPV-seq may be more sensitive than dPCR for MRD detection.

Dental Extractions Before Radiation Therapy and the Risk of Osteoradionecrosis in Patients With Head and Neck Cancer.

Importance: Patients with head and neck cancer undergo extraction of teeth with poor prognoses to minimize post-radiation therapy (RT) extractions, which are known to cause osteoradionecrosis (ORN). However, many patients are required to start RT before the extraction sites are completely healed. The role of pre-RT extractions in the development of ORN has been disputed in literature. Objective: To determine whether the timing of pre-RT dental extractions is associated with ORN development in patients with head and neck cancer. Design, Setting, and Participants: This retrospective cohort study was conducted at a single institution (Princess Margaret Cancer Centre, Toronto, Canada) between January 1, 2011, and January 1, 2018, and included 879 patients with head and neck cancer who underwent pre-RT dental extractions before curative RT of 45 Gy or greater. Patient demographic information and clinical characteristics (eg, primary cancer site, nodal involvement, chemotherapy, smoking status, dental pathology) were considered. Data analyses were performed from July to December 2022. Main outcomes and measures: Timing (number of days) from dental extractions to RT start date and pre-RT extractions categorized as healed, minor bone spicules (MBS), or ORN. Results: The study population consisted of 879 patients with a median (range) age of 62 (20-96) years, with 685 men (78%) and 194 women (22%). Of these, 847 (96.3%) healed from pre-RT dental extractions, 16 (1.8%) developed MBS, and 16 (1.8%) developed ORN. The median (range) time in number of days from pre-RT extraction(s) to start of RT was 9 (0-98) days in the healed cohort, 6 (3-23) days in the MBS cohort, and 6 (0-12) days in the ORN cohort. There was a large difference in the timing of pre-RT extractions between the healed and the MBS cohorts (mean 11.9 vs 7.4 days to radiation; difference 4.4; 95% CI, 1.5-7.3), and the healed and the ORN cohorts (mean 11.9 vs 7.1 days; difference 4.8 days; 95% CI, 2.6-7.1). Conclusion: The findings of this retrospective cohort study suggest that there was an important association between the timing of pre-RT dental extractions and ORN when extractions occurred within 7 days of the RT start date. Despite this, ORN after pre-RT extractions is relatively rare. These findings indicate that patients with head and neck cancer who are to undergo RT should not delay treatment for extractions when it might compromise oncologic control.

Evaluation of clinical decision support systems in oncology: An updated systematic review.

With increasing reliance on technology in oncology, the impact of digital clinical decision support (CDS) tools needs to be examined. A systematic review update was conducted and peer-reviewed literature from 2016 to 2022 were included if CDS tools were used for live decision making and comparatively assessed quantitative outcomes. 3369 studies were screened and 19 were included in this updated review. Combined with a previous review of 24 studies, a total of 43 studies were analyzed. Improvements in outcomes were observed in 42 studies, and 34 of these were of statistical significance. Computerized physician order entry and clinical practice guideline systems comprise the greatest number of evaluated CDS tools (13 and 10 respectively), followed by those that utilize patient-reported outcomes (8), clinical pathway systems (8) and prescriber alerts for best-practice advisories (4). Our review indicates that CDS can improve guideline adherence, patient-centered care, and care delivery processes in oncology.

Development and Standardization of a Classification System for Osteoradionecrosis: Implementation of a Risk-Based Model.

Purpose: Osteoradionecrosis of the jaw (ORN) can manifest in varying severity. The aim of this study is to identify ORN risk factors and develop a novel classification to depict the severity of ORN. Methods: Consecutive head-and-neck cancer (HNC) patients treated with curative-intent IMRT (≥ 45Gy) in 2011-2018 were included. Occurrence of ORN was identified from in-house prospective dental and clinical databases and charts. Multivariable logistic regression model was used to identify risk factors and stratify patients into high-risk and low-risk groups. A novel ORN classification system was developed to depict ORN severity by modifying existing systems and incorporating expert opinion. The performance of the novel system was compared to fifteen existing systems for their ability to identify and predict serious ORN event (jaw fracture or requiring jaw resection). Results: ORN was identified in 219 out of 2732 (8%) consecutive HNC patients. Factors associated with high-risk of ORN were: oral-cavity or oropharyngeal primaries, received IMRT dose ≥60Gy, current/ex-smokers, and/or stage III-IV periodontal disease. The ORN rate for high-risk vs low-risk patients was 12.7% vs 3.1% (p<0.001) with an area-under-the-receiver-operating-curve (AUC) of 0.71. Existing ORN systems overclassified serious ORN events and failed to recognize maxillary ORN. A novel ORN classification system, RadORN, was proposed based on vertical extent of bone necrosis and presence/absence of exposed bone/fistula. This system detected serious ORN events in 5.7% of patients and statistically outperformed existing systems. Conclusion: We identified risk factors for ORN, and proposed a novel ORN classification system based on vertical extent of bone necrosis and presence/absence of exposed bone/fistula. It outperformed existing systems in depicting the seriousness of ORN, and may facilitate clinical care and clinical trials.

Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.

Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targeted clinical trials. Recent advances in ML have enabled the integration of wider ranges of data including both medical records and imaging (radiomics). However, the development of prognostic models is complex as no modeling strategy is universally superior to others and validation of developed models requires large and diverse datasets to demonstrate that prognostic models developed (regardless of method) from one dataset are applicable to other datasets both internally and externally. Using a retrospective dataset of 2,552 patients from a single institution and a strict evaluation framework that included external validation on three external patient cohorts (873 patients), we crowdsourced the development of ML models to predict overall survival in head and neck cancer (HNC) using electronic medical records (EMR) and pretreatment radiological images. To assess the relative contributions of radiomics in predicting HNC prognosis, we compared 12 different models using imaging and/or EMR data. The model with the highest accuracy used multitask learning on clinical data and tumor volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction, outperforming models relying on clinical data only, engineered radiomics, or complex deep neural network architecture. However, when we attempted to extend the best performing models from this large training dataset to other institutions, we observed significant reductions in the performance of the model in those datasets, highlighting the importance of detailed population-based reporting for AI/ML model utility and stronger validation frameworks. We have developed highly prognostic models for overall survival in HNC using EMRs and pretreatment radiological images based on a large, retrospective dataset of 2,552 patients from our institution.Diverse ML approaches were used by independent investigators. The model with the highest accuracy used multitask learning on clinical data and tumor volume.External validation of the top three performing models on three datasets (873 patients) with significant differences in the distributions of clinical and demographic variables demonstrated significant decreases in model performance. Significance: ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.

Implication of dental insurance status on patterns of pre-radiation dental extraction and risk of osteoradionecrosis of the jaw in head-and-neck cancer patients.

Oral toxicities such as osteoradionecrosis can be minimized by dental screening and prophylactic dental care prior to head and neck (HN) radiation therapy (RT). However, limited information is available about how dental insurance interacts with prophylactic dental care and osteoradionecrosis. To address this gap in knowledge, we conducted a cohort study of 2743 consecutive adult patients treated with curative radiation for HN malignancy who underwent pre-radiation dental assessment and where required, prophylactic dental treatment. Charts were reviewed to determine patient demographics, dental findings, dental treatment and development of osteoradionecrosis following radiation. Three insurance cohorts were identified: private-insured (50.4 %), public-insured (7.3 %), being patients with coverage through government-funded disability and welfare programs, and self-pay (42.4 %). More than half the public-insured patients underwent prophylactic pre-radiation dental extractions, followed by self-pay patients (44 %) and private-insured patients (26.6 %). After a median follow-up time of 4.23 years, 6.5 % of patients developed osteoradionecrosis. The actuarial rate of osteoradionecrosis in the public-insured patients was 14.7 % at 5-years post-RT, compared to 7.5 % in private-insured patients and 6.7 % in self-pay patients. On multivariable analysis, dental insurance status, DMFS160, age at diagnosis, sex, tumor site, nodal involvement, years smoked and gross income were all significant risk factors for tooth removal prior to HN radiation. However, only public-insured status, tumor site and years smoked were significant risk factors for development of osteoradionecrosis. Our findings demonstrate that lack of comprehensive dental coverage (patients who self-pay or who have limited coverage under public-insured programs) associates strongly with having teeth removed prior to HN RT. Nearly 1 in 6 patients covered under public-insurance developed osteoradionecrosis within 5 years of completing their treatment. Well-funded dental insurance programs for HN cancer patients might reduce the number of pre-RT extractions performed in these patients, improving quality of life post-RT.