Target Delineation in Postoperative Radiation Therapy for Head and Neck Cancer After Flap Reconstruction.

Delineation of the clinical target volume (CTV) after resection of head and neck cancer can be challenging, especially after flap reconstruction. The main area of contention is whether the entire flap should be included in the CTV. Several case series have reported marginal misses and intraflap failures when the entire flap was not routinely included in the CTV. On the other hand, available data have not convincingly demonstrated a detriment to long-term outcomes using intensity modulated radiotherapy after flap reconstruction. On the contrary, postoperative radiation can facilitate epilation and mucosalization of the flap tissue, reduce flap bulk, and improve long-term esthetic and functional outcomes. Therefore, our standard practice is to include the entire flap in the CTV. In certain scenarios, we may allow for a lower dose to part of flap distant from the resection bed than the flap-tumor bed junction, where recurrences are most likely. We provide three case vignettes describing such scenarios where sparing part of the flap, and more importantly, the nearby uninvolved native tissue, from high-dose radiation may be justified.

In silico evaluation and feasibility of near margin-less head and neck daily adaptive radiotherapy.

OBJECTIVE: We explore the potential dosimetric benefits of reducing treatment volumes through daily adaptive radiation therapy for head and neck cancer (HNC) patients using the Ethos system/Intelligent Optimizer Engine (IOE). We hypothesize reducing treatment volumes afforded by daily adaption will significantly reduce the dose to adjacent organs at risk. We also explore the capability of the Ethos IOE to accommodate this highly conformal approach in HNC radiation therapy. METHODS: Ten HNC patients from a phase II trial were chosen, and their cone-beam CT (CBCT) scans were uploaded to the adaptive RT (ART) emulator. A new initial reference plan was generated using both a 1 mm and 5 mm planning target volume (PTV) expansion. Daily adaptive ART plans (1 mm) were simulated from the clinical CBCT taken every fifth fraction. Additionally, using physician-modified ART contours the larger 5 mm plan was recalculated on this recontoured on daily anatomy. Changes in target and OAR contours were measured using Dice coefficients as a surrogate of clinician effort. PTV coverage and organ-at-risk (OAR) doses were statistically compared, and the robustness of each ART plan was evaluated at fractions 5 and 35 to observe if OAR doses were within 3 Gy of pre-plan. RESULTS: This study involved six patients with oropharynx and four with larynx cancer, totaling 70 adaptive fractions. The primary and nodal gross tumor volumes (GTV) required the most adjustments, with median Dice scores of 0.88 (range: 0.80-0.93) and 0.83 (range: 0.66-0.91), respectively. For the 5th and 35th fraction plans, 80 % of structures met robustness criteria (quartile 1-3: 67-100 % and 70-90 %). Adaptive planning improved median PTV V100% coverage for doses of 70 Gy (96 % vs. 95.6 %), 66.5 Gy (98.5 % vs. 76.5 %), and 63 Gy (98.9 % vs. 74.9 %) (p < 0.03). Implementing ART with total volume reduction yielded median dose reductions of 7-12 Gy to key organs-at-risk (OARs) like submandibular glands, parotids, oral cavity, and constrictors (p < 0.05). CONCLUSIONS: The IOE enables feasible daily ART treatments with reduced margins while enhancing target coverage and reducing OAR doses for HNC patients. A phase II trial recently finished accrual and forthcoming analysis will determine if these dosimetric improvements correlate with improved patient-reported outcomes.

In Silico Analysis of Adjuvant Head and Neck Online Adaptive Radiation Therapy.

Purpose: Recently developed online adaptive radiation therapy (OnART) systems enable frequent treatment plan adaptation, but data supporting a dosimetric benefit in postoperative head and neck radiation therapy (RT) are sparse. We performed an in silico dosimetric study to assess the potential benefits of a single versus weekly OnART in the treatment of patients with head and neck squamous cell carcinoma in the adjuvant setting. Methods and Materials: Twelve patients receiving conventionally fractionated RT over 6 weeks and 12 patients receiving hypofractionated RT over 3 weeks on a clinical trial were analyzed. The OnART emulator was used to virtually adapt either once midtreatment or weekly based on the patient's routinely performed cone beam computed tomography. The planning target volume (PTV) coverage, dose heterogeneity, and cumulative dose to the organs at risk for these 2 adaptive approaches were compared with the nonadapted plan. Results: In total, 13, 8, and 3 patients had oral cavity, oropharynx, and larynx primaries, respectively. In the conventionally fractionated RT cohort, weekly OnART led to a significant improvement in PTV V100% coverage (6.2%), hot spot (-1.2 Gy), and maximum cord dose (-3.1 Gy), whereas the mean ipsilateral parotid dose increased modestly (1.8 Gy) versus the nonadapted plan. When adapting once midtreatment, PTV coverage improved with a smaller magnitude (0.2%-2.5%), whereas dose increased to the ipsilateral parotid (1.0-1.1 Gy) and mandible (0.2-0.7 Gy). For the hypofractionated RT cohort, similar benefit was observed with weekly OnART, including significant improvement in PTV coverage, hot spot, and maximum cord dose, whereas no consistent dosimetric advantage was seen when adapting once midtreatment. Conclusions: For head and neck squamous cell carcinoma adjuvant RT, there was a limited benefit of single OnART, but weekly adaptations meaningfully improved the dosimetric criteria, predominantly PTV coverage and dose heterogeneity. A prospective study is ongoing to determine the clinical benefit of OnART in this setting.

Early Experience of Online Adaptive Radiation Therapy for Definitive Radiation of Patients With Head and Neck Cancer.

Purpose: The advent of cone beam computed tomography-based online adaptive radiation therapy (oART) has dramatically reduced the barriers of adaptation. We present the first prospective oART experience data in radiation of head and neck cancers (HNC). Methods and Materials: Patients with HNC receiving definitive standard fractionation (chemo)radiation who underwent at least 1 oART session were enrolled in a prospective registry study. The frequency of adaptations was at the discretion of the treating physician. Physicians were given the option of delivering 1 of 2 plans during adaptation: the original radiation plan transposed onto the cone beam computed tomography with adapted contours (scheduled), and a new adapted plan generated from the updated contours (adapted). A paired t test was used to compare the mean doses between scheduled and adapted plans. Results: Twenty-one patients (15 oropharynx, 4 larynx/hypopharynx, 2 other) underwent 43 adaptation sessions (median, 2). The median ART process time was 23 minutes, median physician time at the console was 27 minutes, and median patient time in the vault was 43.5 minutes. The adapted plan was chosen 93% of the time. The mean volume in each planned target volume (PTV) receiving 100% of the prescription dose for the scheduled versus adapted plan for high-risk PTVs was 87.8% versus 95% (P < .01), intermediate-risk PTVs was 87.3% versus 97.9% (P < .01), and low-risk PTVs was 94% versus 97.8% (P < .01), respectively. The mean hotspot was also lower with adaptation: 108.8% versus 106.4% (P < .01). All but 1 organ at risk (11/12) saw a decrease in their dose with the adapted plans, with the mean ipsilateral parotid (P = .013), mean larynx (P < .01), maximum point spinal cord (P < .01), and maximum point brain stem (P = .035) reaching statistical significance. Conclusions: Online ART is feasible for HNC, with significant improvement in target coverage and homogeneity and a modest decrease in doses to several organs at risk.

Long-term Patient-Reported Outcomes in a Population-Based Cohort Following Radiotherapy vs Surgery for Oropharyngeal Cancer.

Importance: Oncologic outcomes are similar for patients with oropharyngeal squamous cell carcinoma (OPSCC) treated with primary surgery or radiotherapy. However, comparative differences in long-term patient-reported outcomes (PROs) between modalities are less well established. Objective: To determine the association between primary surgery or radiotherapy and long-term PROs. Design, Setting, and Participants: This cross-sectional study used the Texas Cancer Registry to identify survivors of OPSCC treated definitively with primary radiotherapy or surgery between January 1, 2006, and December 31, 2016. Patients were surveyed in October 2020 and April 2021. Exposures: Primary radiotherapy and surgery for OPSCC. Main Outcomes and Measures: Patients completed a questionnaire that included demographic and treatment information, the MD Anderson Symptom Inventory-Head and Neck (MDASI-HN) module, the Neck Dissection Impairment Index (NDII), and the Effectiveness of Auditory Rehabilitation (EAR) scale. Multivariable linear regression models were performed to evaluate the association of treatment (surgery vs radiotherapy) with PROs while controlling for additional variables. Results: Questionnaires were mailed to 1600 survivors of OPSCC identified from the Texas Cancer Registry, with 400 responding (25% response rate), of whom 183 (46.2%) were 8 to 15 years from their initial diagnosis. The final analysis included 396 patients (aged ≤57 years, 190 [48.0%]; aged >57 years, 206 [52.0%]; female, 72 [18.2%]; male, 324 [81.8%]). After multivariable adjustment, no significant differences were found between surgery and radiotherapy outcomes as measured by the MDASI-HN (ß, -0.1; 95% CI, -0.7 to 0.6), NDII (ß, -1.7; 95% CI, -6.7 to 3.4), and EAR (ß, -0.9; 95% CI -7.7 to 5.8). In contrast, less education, lower household income, and feeding tube use were associated with significantly worse MDASI-HN, NDII, and EAR scores, while concurrent chemotherapy with radiotherapy was associated with worse MDASI-HN and EAR scores. Conclusions and Relevance: This population-based cohort study found no associations between long-term PROs and primary radiotherapy or surgery for OPSCC. Lower socioeconomic status, feeding tube use, and concurrent chemotherapy were associated with worse long-term PROs. Further efforts should focus on the mechanism, prevention, and rehabilitation of these long-term treatment toxicities. The long-term outcomes of concurrent chemotherapy should be validated and may inform treatment decision making.

Efficacy and Quality-of-Life Following Involved Nodal Radiotherapy for Head and Neck Squamous Cell Carcinoma: The INRT-AIR Phase II Clinical Trial.

PURPOSE: Elective neck irradiation (ENI) has long been considered mandatory when treating head and neck squamous cell carcinoma (HNSCC) with definitive radiotherapy, but it is associated with significant dose to normal organs-at-risk (OAR). In this prospective phase II study, we investigated the efficacy and tolerability of eliminating ENI and strictly treating involved and suspicious lymph nodes (LN) with intensity-modulated radiotherapy. PATIENTS AND METHODS: Patients with newly diagnosed HNSCC of the oropharynx, larynx, and hypopharynx were eligible for enrollment. Each LN was characterized as involved or suspicious based on radiologic criteria and an in-house artificial intelligence (AI)-based classification model. Gross disease received 70 Gray (Gy) in 35 fractions and suspicious LNs were treated with 66.5 Gy, without ENI. The primary endpoint was solitary elective volume recurrence, with secondary endpoints including patterns-of-failure and patient-reported outcomes. RESULTS: Sixty-seven patients were enrolled, with 18 larynx/hypopharynx and 49 oropharynx cancer. With a median follow-up of 33.4 months, the 2-year risk of solitary elective nodal recurrence was 0%. Gastrostomy tubes were placed in 14 (21%), with median removal after 2.9 months for disease-free patients; no disease-free patient is chronically dependent. Grade I/II dermatitis was seen in 90%/10%. There was no significant decline in composite MD Anderson Dysphagia Index scores after treatment, with means of 89.1 and 92.6 at 12 and 24 months, respectively. CONCLUSIONS: These results suggest that eliminating ENI is oncologically sound for HNSCC, with highly favorable quality-of-life outcomes. Additional prospective studies are needed to support this promising paradigm before implementation in any nontrial setting.

ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer.

Objective. Accurate diagnosis of lymph node metastasis (LNM) is critical in treatment management for patients with head and neck cancer. Positron emission tomography and computed tomography are routinely used for identifying LNM status. However, for small or less fluorodeoxyglucose (FDG) avid nodes, there are always uncertainties in LNM diagnosis. We are aiming to develop a reliable prediction model is for identifying LNM.Approach. In this study, a new automated and reliable multi-objective learning model (ARMO) is proposed. In ARMO, a multi-objective model is introduced to obtain balanced sensitivity and specificity. Meanwhile, confidence is calibrated by introducing individual reliability, whilst the model uncertainty is estimated by a newly defined overall reliability in ARMO. In the training stage, a Pareto-optimal model set is generated. Then all the Pareto-optimal models are used, and a reliable fusion strategy that introduces individual reliability is developed for calibrating the confidence of each output. The overall reliability is calculated to estimate the model uncertainty for each test sample.Main results. The experimental results demonstrated that ARMO obtained more promising results, which the area under the curve, accuracy, sensitivity and specificity can achieve 0.97, 0.93, 0.88 and 0.94, respectively. Meanwhile, based on calibrated confidence and overall reliability, clinicians could pay particular attention to highly uncertain predictions.Significance. In this study, we developed a unified model that can achieve balanced prediction, confidence calibration and uncertainty estimation simultaneously. The experimental results demonstrated that ARMO can obtain accurate and reliable prediction performance.

Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation.

PURPOSE: Varian Ethos utilizes novel intelligent-optimization-engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine-learning-guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART). METHODS: Twenty previously treated patients treated on C-arm/Ring-mounted were retroactively re-planned in the Ethos planning system using a fixed 18-beam intensity-modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in-house deep-learning 3D-dose predictor (AI-Guided) (2) commercial knowledge-based planning (KBP) model with universal RTOG-based population criteria (KBP-RTOG) and (3) an RTOG-based constraint template only (RTOG) for in-depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH-estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high-impact organs-at-risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two-tailed student t-test. RESULTS: AI-guided plans were superior to both KBP-RTOG and RTOG-only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable or improved with AI-guided plans versus benchmark, while they increased with KBP-RTOG and RTOG plans. However, all plans generally satisfied the RTOG criteria. Heterogeneity Index (HI) was on average <1.07 for all plans. Average modulation factor was 12.2 ± 1.9 (p = n.s), 13.1 ± 1.4 (p = <0.001), 11.5 ± 1.3 (p = n.s.) and 12.2 ± 1.9 for KBP-RTOG, AI-Guided, RTOG and benchmark plans, respectively. CONCLUSION: AI-guided plans were the highest quality. Both KBP-enabled and RTOG-only plans are feasible approaches as clinics adopt ART workflows. Similar to constrained optimization, the IOE is sensitive to clinical input goals and we recommend comparable input to an institution's planning directive dosimetric criteria.

Artificial intelligence guided physician directive improves head and neck planning quality and practice Uniformity: A prospective study.

•AI dose predictor was fully integrated with treatment planning system and used as a physicain decision support tool to improve uniformity of practice.•Model was trained based on our standard of practice, but implemented at the time of expansion with 3 new physicians join the practice.•Phase 1 retrospective evaluation demonstrated the non-uniform practice among 3 MDs and only 52.9% frequency planner can achieve physicians' directives.•Significant improvement in practice uniformity of practice was observed after utilizing AI as DST and 80.4% frequency clinical plan can achieve AI-guided physician directives.

Expert Consensus Statement: Management of Dysphagia in Head and Neck Cancer Patients.

OBJECTIVE: To develop an expert consensus statement (ECS) on the management of dysphagia in head and neck cancer (HNC) patients to address controversies and offer opportunities for quality improvement. Dysphagia in HNC was defined as swallowing impairment in patients with cancers of the nasal cavity, paranasal sinuses, nasopharynx, oral cavity, oropharynx, larynx, or hypopharynx. METHODS: Development group members with expertise in dysphagia followed established guidelines for developing ECS. A professional search strategist systematically reviewed the literature, and the best available evidence was used to compose consensus statements targeted at providers managing dysphagia in adult HNC populations. The development group prioritized topics where there was significant practice variation and topics that would improve the quality of HNC patient care if consensus were possible. RESULTS: The development group identified 60 candidate consensus statements, based on 75 initial proposed topics and questions, that focused on addressing the following high yield topics: (1) risk factors, (2) screening, (3) evaluation, (4) prevention, (5) interventions, and (6) surveillance. After 2 iterations of the Delphi survey and the removal of duplicative statements, 48 statements met the standardized definition for consensus; 12 statements were designated as no consensus. CONCLUSION: Expert consensus was achieved for 48 statements pertaining to risk factors, screening, evaluation, prevention, intervention, and surveillance for dysphagia in HNC patients. Clinicians can use these statements to improve quality of care, inform policy and protocols, and appreciate areas where there is no consensus. Future research, ideally randomized controlled trials, is warranted to address additional controversies related to dysphagia in HNC patients.

Patient-Reported Financial Toxicity in a Population-Based Cohort of Oropharynx Cancer Survivors.

PURPOSE: Patients with oropharyngeal squamous cell carcinoma (OPSCC) are at high risk for financial toxicity (FT), but the nature, extent, and predictors of FT experienced after primary radiation therapy (RT) or surgery are poorly understood. METHODS AND MATERIALS: We used a population-based sample of patients from the Texas Cancer Registry with stage I to III OPSCC diagnosed from 2006 to 2016 and treated with primary RT or surgery. Of 1,668 eligible patients, 1,600 were sampled, 400 responded, and 396 confirmed OPSCC. Measures included the MD Anderson Symptom Inventory Head and Neck, Neck Dissection Impairment Index, and a financial toxicity instrument adapted from the Individualized Cancer Care (iCanCare) study. Multivariable logistic regression evaluated associations of exposures with outcomes. RESULTS: Of 396 analyzable respondents, 269 (68%) received primary RT and 127 (32%) surgery. The median time from diagnosis to survey was 7 years. Due to OPSCC, 54% of patients experienced material sacrifice (including 28% who reduced food spending and 6% who lost their housing), 45% worried about financial problems, and 29% experienced long-term FT. Independent factors associated with more long-term FT included female sex (odds ratio [OR], 1.72; 95% confidence interval [CI], 1.23-2.40), Black non-Hispanic race (OR, 2.98; 95% CI, 1.26-7.09), unmarried status (OR, 1.50; 95% CI, 1.11-2.03), feeding tube use (OR, 3.98; 95% CI, 2.29-6.90), and worst versus best quartile on the MD Anderson Symptom Inventory Head and Neck (OR, 1.89; 95% CI, 1.23-2.90) and Neck Dissection Impairment Index (OR, 5.62; 95% CI, 3.79-8.34). Factors associated with less long-term FT included age >57 years (OR, 0.54; 95% CI, 0.41-0.71; P < .001) and household income ≥$80,000 (OR, 0.60; 95% CI, 0.44-0.82; P = .001). Primary RT versus surgery was not associated with long-term FT (OR, 0.92; 95% CI, 0.68-1.24). CONCLUSIONS: Oropharynx cancer survivors experience high rates of material sacrifice and long-term FT, and we identified important risk factors. Chronic symptom burden was associated with significantly worse long-term financial status, supporting the hypothesis that toxicity mitigation strategies may reduce long-term FT.

PREVLAR: Phase 2a Randomized Trial to Assess the Safety and Efficacy of RRx-001 in the Attenuation of Oral Mucositis in Patients Receiving Head and Neck Chemoradiotherapy.

PURPOSE: No Food and Drug Administration-approved intervention exists for oral mucositis (OM) from chemoradiotherapy (CRT) used to treat head and neck cancers. RRx-001 is a hypoxia-activated, cysteine-directed molecule that affects key pathways involved in OM pathogenesis. This phase 2a, multi-institutional trial was designed to assess the safety and feasibility of 3 schedules of a fixed concentration of RRx-001; a standard-of-care arm was included to identify potential signals of efficacy for further study. METHODS AND MATERIALS: This study enrolled patients with oral cavity and oropharynx squamous cell carcinoma receiving definitive or postoperative cisplatin-based CRT. Patients were randomized into 4 cohorts. In arms 1 to 3, RRx-001 was coinfused with patients' blood at differing intervals. Arm 4 was a control cohort of patients treated with CRT alone. Trained evaluators assessed OM using a standardized data collection instrument twice weekly during treatment and then until resolution. OM severity was scored centrally using World Health Organization criteria. Safety outcomes were assessed using National Cancer Institute - Common Terminology Criteriav4 benchmarks. Long-term tumor response was defined by Response evaluation criteria in solid tumors v1.1 criteria. RESULTS: Fifty-three patients were enrolled, with 46 and 45 individuals contributing safety and efficacy data, respectively. There were no severe adverse events attributed to the study drug. Across all 3 active arms, the study drug was infused fully per protocol in 86% of patients. All 3 RRx-001 treatment cohorts appeared to demonstrate a similar or lower OM duration relative to control; arm 1 had the lowest median duration of severe oral mucositis (SOM), 8.5 days versus 24 days in controls among patients who developed at least 1 day of SOM. There were no locoregional failures in any patient. CONCLUSIONS: Our results support the safety and feasibility of RRx-001 as an intervention to mitigate SOM. Additional studies are planned to confirm its efficacy.

Preliminary Evaluation of PTV Margins for Online Adaptive Radiation Therapy of the Prostatic Fossa.

PURPOSE: In modern trials, traditional planning target volume (PTV) margins for postoperative prostate radiation therapy have been large (7-10 mm) to account for both daily changes in patient positioning and target deformation. With daily adaptive radiation therapy, these interfractional changes could be minimized, potentially reducing the margins required for treatment and improving adjacent normal-tissue dosimetry. METHODS AND MATERIALS: A single-center retrospective study was conducted from March 2021 to November 2021. Patients receiving conventionally fractionated postoperative radiation therapy (PORT) for prostate cancer with pretreatment and posttreatment cone beam computed tomography (CBCT) imaging (pre-CBCT and post-CBCT, respectively) were included (248 paired images). Pretreatment and posttreatment clinical target volumes (pre-CTVs and post-CTVs) were contoured by a single observer on all CBCTs and verified by a second observer. Motion was calculated from pre-CTV to that of the post-CTV, and predicted margins were calculated with van Herk's formula. Adequate coverage of the proposed planning target volume (PTV) margin expansions (pre-PTV) were verified by determining overlap with post-CTV. In a smaller cohort (25 paired images), dosimetric changes with the proposed online adaptive margins were compared with conventional plans in the Ethos emulator environment. RESULTS: The estimated margins predicted to achieve ≥95% CTV coverage for 90% of the population were 1.6 mm, 2.0 mm, and 2.2 mm (x-, y-, and z -xes, respectively), with 95% of the absolute region of interest displacement being within 1.9 mm, 2.8 mm, and 2.1 mm. After symmetrically expanding all pre-CTVs by 3 mm, the percentage of paired images achieving ≥95% CTV coverage was 97.1%. When comparing adaptive plans (3-mm margins) with scheduled plans (7-mm margins), rectum dosimetry significantly improved, with an average relative reduction in V40Gy[cc] of 59.2% and V65Gy[cc] of 79.5% (where V40Gy and V65Gy are defined as the volumes receiving 40 Gy and 65 Gy or higher dose, respectively). CONCLUSIONS: Online daily adaptive radiation therapy could significantly decrease PTV margins for prostatic PORT and improve rectal dosimetry, with a symmetrical expansion of 3 mm achieving excellent coverage in this cohort. These results need to be validated in a larger prospective cohort.

Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option.

PURPOSE: A reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients who received radiotherapy. This work aims to develop a delta-radiomics feature-based multi-classifier, multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC LRR prediction. Furthermore, we aim to adopt a learning with rejection option (LRO) strategy to boost the reliability of Delta-mCOM model by rejecting prediction for samples with high prediction uncertainties. METHODS: In this retrospective study, we collected PET/CT image and clinical data from 224 HNC patients who received radiotherapy (RT) at our institution. We calculated the differences between radiomics features extracted from PET/CT images acquired before and after radiotherapy and used them in conjunction with pre-treatment radiomics features as the input features. Using clinical parameters, PET radiomics features, and CT radiomics features, we built and optimized three separate single-modality models. We used multiple classifiers for model construction and employed sensitivity and specificity simultaneously as the training objectives for each of them. Then, for testing samples, we fused the output probabilities from all these single-modality models to obtain the final output probabilities of the Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric uncertainties when predicting with a trained Delta-mCOM model and identified patients associated with prediction of higher reliability (low uncertainty estimates). The epistemic and aleatoric uncertainties were estimated using an AutoEncoder-style anomaly detection model and test-time augmentation (TTA) with predictions made from the Delta-mCOM model, respectively. Predictions with higher epistemic uncertainty or higher aleatoric uncertainty than given thresholds were deemed unreliable, and they were rejected before providing a final prediction. In this study, different thresholds corresponding to different low-reliability prediction rejection ratios were applied. Their values are based on the estimated epistemic and aleatoric uncertainties distribution of the validation data. RESULTS: The Delta-mCOM model performed significantly better than the single-modality models, whether trained with pre-, post-treatment radiomics features or concatenated BaseLine and Delta-Radiomics Features (BL-DRFs). It was numerically superior to the PET and CT fused BL-DRF model (nonstatistically significant). Using the LRO strategy for the Delta-mCOM model, most of the evaluation metrics improved as the rejection ratio increased from 0% to around 25%. Utilizing both epistemic and aleatoric uncertainty for rejection yielded nonstatistically significant improved metrics compared to each alone at approximately a 25% rejection ratio. Metrics were significantly better than the no-rejection method when the reject ratio was higher than 50%. CONCLUSIONS: The inclusion of the delta-radiomics feature improved the accuracy of HNC LRR prediction, and the proposed Delta-mCOM model can give more reliable predictions by rejecting predictions for samples of high uncertainty using the LRO strategy.

Adherence to the American Cancer Society Head and Neck Cancer Survivorship Care Guideline According to Chart Review: A Nested Retrospective Cohort Pilot Study.

OBJECTIVES: The purpose of this study was to explore adherence to the American Cancer Society (ACS) Head and Neck Cancer (HNC) Survivorship Care Guideline and their outlined 33 recommendations among posttreatment HNC survivors. METHODS: A bi-institutional, retrospective, nested cohort study of mucosal or salivary gland HNC survivors diagnosed in 2018 was designed. Guideline adherence was assessed via retrospective chart review between 0 and 13 months after completion of oncologic treatment according to 4 categories: (1) problem assessed, (2) problem diagnosed, (3) management offered; (4) problem treated. Adherence was defined as meeting a recommendation subcategory at least once over the 13-month period. RESULTS: Among 60 randomly selected HNC survivors, a total of 38 were included in the final cohort after exclusion of individuals with ineligible cancers and those who died or were lost to follow-up over the study period. Approximately 95% of HNC survivors were assessed for HNC recurrence and screened for lung cancer. Certain common problems such as xerostomia, dysphagia, and hypothyroidism were screened for and managed in ≥70% of eligible survivors. Conversely, screening for other second primary cancers and assessment of a majority of other physical and psychosocial harms occurred in <70% of survivors, and in many cases none to a slim minority of survivors (eg, sleep apnea and sleep disturbance, body and self-image concerns). Only 5% of survivors received a survivorship care plan. CONCLUSION: Overall adherence to the ACS HNC Survivorship Care Guideline in early posttreatment survivors was suboptimal. Interventions are needed to better implement and operationalize these guideline recommendations.

A Proof-of-Concept Study of Artificial Intelligence-assisted Contour Editing.

Purpose: To present a concept called artificial intelligence-assisted contour editing (AIACE) and demonstrate its feasibility. Materials and Methods: The conceptual workflow of AIACE is as follows: Given an initial contour that requires clinician editing, the clinician indicates where large editing is needed, and a trained deep learning model uses this input to update the contour. This process repeats until a clinically acceptable contour is achieved. In this retrospective, proof-of-concept study, the authors demonstrated the concept on two-dimensional (2D) axial CT images from three head-and-neck cancer datasets by simulating the interaction with the AIACE model to mimic the clinical environment. The input at each iteration was one mouse click on the desired location of the contour segment. Model performance is quantified with the Dice similarity coefficient (DSC) and 95th percentile of Hausdorff distance (HD95) based on three datasets with sample sizes of 10, 28, and 20 patients. Results: The average DSCs and HD95 values of the automatically generated initial contours were 0.82 and 4.3 mm, 0.73 and 5.6 mm, and 0.67 and 11.4 mm for the three datasets, which were improved to 0.91 and 2.1 mm, 0.86 and 2.5 mm, and 0.86 and 3.3 mm, respectively, with three mouse clicks. Each deep learning-based contour update required about 20 msec. Conclusion: The authors proposed the newly developed AIACE concept, which uses deep learning models to assist clinicians in editing contours efficiently and effectively, and demonstrated its feasibility by using 2D axial CT images from three head-and-neck cancer datasets.Keywords: Segmentation, Convolutional Neural Network (CNN), CT, Deep Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.

Advances in Automated Treatment Planning.

Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.