Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.

BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer. METHODS: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer", "Pain", "Pain Management", "Analgesics", "Artificial Intelligence", "Machine Learning", and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines. RESULTS: Forty four studies from 2006-2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%). CONCLUSION: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer.

PURPOSE: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value variables, which, for radiation-induced xerostomia, are baseline xerostomia and mean salivary gland doses. This study aimed to improve the prediction of late xerostomia by using 3-dimensional information from radiation dose distributions, computed tomography imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL). METHODS AND MATERIALS: An international cohort of 1208 patients with head and neck cancer from 2 institutes was used to train and twice validate DL models (deep convolutional neural network, EfficientNet-v2, and ResNet) with 3-dimensional dose distribution, computed tomography scan, organ-at-risk segmentations, baseline xerostomia score, sex, and age as input. The NTCP endpoint was moderate-to-severe xerostomia 12 months postradiation therapy. The DL models' prediction performance was compared with a reference model: a recently published xerostomia NTCP model that used baseline xerostomia score and mean salivary gland doses as input. Attention maps were created to visualize the focus regions of the DL predictions. Transfer learning was conducted to improve the DL model performance on the external validation set. RESULTS: All DL-based NTCP models showed better performance (area under the receiver operating characteristic curve [AUC]test, 0.78-0.79) than the reference NTCP model (AUCtest, 0.74) in the independent test. Attention maps showed that the DL model focused on the major salivary glands, particularly the stem cell-rich region of the parotid glands. DL models obtained lower external validation performance (AUCexternal, 0.63) than the reference model (AUCexternal, 0.66). After transfer learning on a small external subset, the DL model (AUCtl, external, 0.66) performed better than the reference model (AUCtl, external, 0.64). CONCLUSION: DL-based NTCP models performed better than the reference model when validated in data from the same institute. Improved performance in the external data set was achieved with transfer learning, demonstrating the need for multicenter training data to realize generalizable DL-based NTCP models.

A prospective cohort study on stereotactic radiotherapy in the management of dural recurrence of olfactory neuroblastoma.

BACKGROUND: Treatment for dural recurrence of olfactory neuroblastoma (ONB) is not standardized. We assess the outcomes of stereotactic body radiotherapy (SBRT) in this population. METHODS: ONB patients with dural recurrences treated between 2013 and 2022 on a prospective registry were included. Tumor control, survival, and patient-reported quality of life were analyzed. RESULTS: Fourteen patients with 32 dural lesions were evaluated. Time to dural recurrence was 58.3 months. Thirty lesions (94%) were treated with SBRT to a median dose of 27 Gy in three fractions. Two patients (3 of 32 lesions; 9%) developed in-field radiographic progression, five patients (38%) experienced progression in non-contiguous dura. Two-year local control was 85% (95% CI: 51-96%). There were no >grade 3 acute toxicities and 1 case of late grade 3 brain radionecrosis. CONCLUSION: In this largest study of SBRT reirradiation for ONB dural recurrence to date, high local control rates with minimal toxicity were attainable.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context.

Purpose: Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan. Methods and Materials: Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot. To validate our scoring method, 6 physicians were recruited to assess 60 plans, each using a scoring table consisting of a 5-point Likert scale (with scores ≥3 considered passing). Spearman correlation analysis was conducted to assess the association between increasing treatment plan percentile rank and physician rating, with Likert scores of 1 and 2 representing clinically unacceptable plans, scores of 3 and 4 representing plans needing minor edits, and a score of 5 representing clinically acceptable plans. Receiver operating characteristic curve analysis was used to assess the scoring system's ability to quantify plan quality. Results: Of the 60 plans scored by the physicians, 8 were deemed as clinically acceptable; these plans had an 89.0th ± 14.5 percentile value using our scoring system. The plans needing minor edits or deemed unacceptable had more variation, with scores falling in the 62.6nd ± 25.1 percentile and 35.6th ± 25.7 percentile, respectively. The estimated Spearman correlation coefficient between the physician score and treatment plan percentile was 0.53 (P < .001), indicating a moderate but statistically significant correlation. Receiver operating characteristic curve analysis demonstrated discernment between acceptable and unacceptable plan quality, with an area under the curve of 0.76. Conclusions: Our scoring system correlates with physician ratings while providing intuitive visual feedback for identifying good treatment plan quality, thereby indicating its utility in the quality assurance process.

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results: We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets. Conclusion: Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

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.

MR-guided stereotactic radiation therapy for head and neck cancers.

Purpose: MR-guided radiotherapy (MRgRT) has the advantage of utilizing high soft tissue contrast imaging to track daily changes in target and critical organs throughout the entire radiation treatment course. Head and neck (HN) stereotactic body radiation therapy (SBRT) has been increasingly used to treat localized lesions within a shorter timeframe. The purpose of this study is to examine the dosimetric difference between the step-and-shot intensity modulated radiation therapy (IMRT) plans on Elekta Unity and our clinical volumetric modulated arc therapy (VMAT) plans on Varian TrueBeam for HN SBRT. Method: Fourteen patients treated on TrueBeam sTx with VMAT treatment plans were re-planned in the Monaco treatment planning system for Elekta Unity MR-Linac (MRL). The plan qualities, including target coverage, conformity, homogeneity, nearby critical organ doses, gradient index and low dose bath volume, were compared between VMAT and Monaco IMRT plans. Additionally, we evaluated the Unity adaptive plans of adapt-to-position (ATP) and adapt-to-shape (ATS) workflows using simulated setup errors for five patients and assessed the outcomes of our treated patients. Results: Monaco IMRT plans achieved comparable results to VMAT plans in terms of target coverage, uniformity and homogeneity, with slightly higher target maximum and mean doses. The critical organ doses in Monaco IMRT plans all met clinical goals; however, the mean doses and low dose bath volumes were higher than in VMAT plans. The adaptive plans demonstrated that the ATP workflow may result in degraded target coverage and OAR doses for HN SBRT, while the ATS workflow can maintain the plan quality. Conclusion: The use of Monaco treatment planning and online adaptation can achieve dosimetric results comparable to VMAT plans, with the additional benefits of real-time tracking of target volume and nearby critical structures. This offers the potential to treat aggressive and variable tumors in HN SBRT and improve local control and treatment toxicity.

Clinical and prognostic differences in oropharyngeal squamous cell carcinoma in USA and Denmark, two HPV high-prevalence areas.

BACKGROUND: Uncertainty persists regarding clinical and treatment variations crucial to consider when comparing high human papillomavirus (HPV)-prevalence oropharyngeal squamous cell carcinoma (OPSCC) cohorts for accurate patient stratification and replicability of clinical trials across different geographical areas. METHODS: OPSCC patients were included from The University of Texas MD Anderson Cancer Center (UTMDACC), USA and from The University Hospital of Copenhagen, Denmark from 2015-2020, (n = 2484). Outcomes were 3-year overall survival (OS) and recurrence-free interval (RFI). Subgroup analyses were made for low-risk OPSCC patients (T1-2N0M0) and high-risk patients (UICC8 III-IV). RESULTS: There were significantly more HPV-positive (88.2 % vs. 63.1 %), males (89.4 % vs. 74.1 %), never-smokers (52.1 % vs. 23.7 %), lower UICC8-stage (I/II: 79.3 % vs. 68 %), and fewer patients treated with radiotherapy (RT) alone (14.8 % vs. 30.3 %) in the UTMDACC cohort. No difference in the adjusted OS was observed (hazard ratio [HR] 1.21, p = 0.23), but a significantly increased RFI HR was observed for the Copenhagen cohort (HR: 1.74, p = 0.003). Subgroup analyses of low- and high-risk patients revealed significant clinical and treatment differences. No difference in prognosis was observed for low-risk patients, but the prognosis for high-risk patients in the Copenhagen cohort was worse (OS HR 2.20, p = 0.004, RFI HR 2.80, p = 0.002). CONCLUSIONS: We identified significant differences in clinical characteristics, treatment modalities, and prognosis between a Northern European and Northern American OPSCC population. These differences are important to consider when comparing outcomes and for patient stratification in clinical trials, as reproducibility might be challenging.

Neoadjuvant Chemotherapy Alone or Combined with EGFR-Directed Targeted Therapy or Anti-PD-1 Immunotherapy for Locally Advanced Lacrimal Sac and Nasolacrimal Duct Carcinomas.

BACKGROUND/AIMS: We describe our findings in patients with locally advanced lacrimal sac and nasolacrimal duct (NLD) carcinoma who received neoadjuvant systemic therapy. METHODS: We identified patients with locally advanced primary lacrimal sac/NLD carcinoma treated with neoadjuvant systemic intravenous therapy at our institution during 2017-2019. RESULTS: The study included seven patients, four men and three women; the mean age was 60.4 years (range: 43-76). All patients had locally advanced disease with significant orbital soft tissue invasion with or without skull base invasion making eye-sparing surgery not feasible as an initial step. Three patients had poorly differentiated squamous cell carcinoma; two, invasive carcinoma with basaloid and squamous features; one, high-grade carcinoma with features suggestive of sebaceous differentiation; and one, undifferentiated carcinoma. The neoadjuvant regimens were cisplatin and docetaxel (n = 1); carboplatin and docetaxel (n = 1); paclitaxel and cetuximab (n = 1); carboplatin, paclitaxel, and cetuximab (EGFR inhibitor) (n = 2); cisplatin, docetaxel, and pembrolizumab (anti-PD-1 immunotherapy) (n = 1); and carboplatin, paclitaxel, and pembrolizumab (n = 1). All patients had radiologic disease regression, and one patient had radiologic near-complete response. After neoadjuvant therapy, all patients underwent wide local excision and adjuvant concurrent chemoradiation. Two patients had a complete pathologic response. At a median follow-up period of 13 months after chemoradiation (range, 8-54 months), all patients were alive without evidence of disease. One patient had nodal metastasis treated with lymph node dissection and adjuvant chemoradiation. CONCLUSIONS: Neoadjuvant systemic therapy can shrink tumors in patients with locally advanced primary lacrimal sac/NLD carcinoma with orbital or skull base invasion.

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods: A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results: For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion: These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

Optimizing Outpatient Radiation Oncology Consult Workflow by Using Time-Driven Activity-Based Costing: Efficiency and Financial Impacts.

PURPOSE: Clinical efficiency is a key component of value-based health care. Our objective here was to identify workflow inefficiencies by using time-driven activity-based costing (TDABC) and evaluate the implementation of a new clinical workflow in high-volume outpatient radiation oncology clinics. METHODS: Our quality improvement study was conducted with the Departments of GI, Genitourinary (GU), and Thoracic Radiation Oncology at a large academic cancer center and four community network sites. TDABC was used to create process maps and optimize workflow for outpatient consults. Patient encounter metrics were captured with a real-time status function in the electronic medical record. Time metrics were compared using Mann-Whitney U tests. RESULTS: Individual patient encounter data for 1,328 consults before the intervention and 1,234 afterward across all sections were included. The median overall cycle time was reduced by 21% in GI (19 minutes), 18% in GU (16 minutes), and 12% at the community sites (9 minutes). The median financial savings per consult were $52 in US dollars (USD) for the GI, $33 USD for GU, $30 USD for thoracic, and $42 USD for the community sites. Patient satisfaction surveys (from 127 of 228 patients) showed that 99% of patients reported that their providers spent adequate time with them and 91% reported being seen by a care provider in a timely manner. CONCLUSION: TDABC can effectively identify opportunities to improve clinical efficiency. Implementing workflow changes on the basis of our findings led to substantial reductions in overall encounter cycle times across several departments, as well as high patient satisfaction and significant financial savings.

Revisiting Feeding Tube Utilization in Oropharynx Cancer: 6-Year Prospective Registry Analysis.

OBJECTIVE: Patients treated for oropharyngeal cancer (OPC) have historically demonstrated high feeding tube rates for decreased oral intake and malnutrition. We re-examined feeding tube practices in these patients. STUDY DESIGN: Retrospective analysis of prospective cohort from 2015 to 2021. SETTING: Single-institution NCI-Designated Comprehensive Cancer Center. METHODS: With IRB approval, patients with new oropharyngeal squamous cell cancer or (unknown primary with neck metastasis) were enrolled. Baseline swallowing was assessed via videofluoroscopy and Performance Status Scale for Head and Neck Cancer (PSSHN). G-tubes or nasogastric tubes (NGT) were placed for weight loss before, during, or after treatment. Prophylactic NGT were placed during transoral robotic surgery (TORS). Tube duration was censored at last disease-free follow-up. Multivariate regression was performed for G-tube placement (odds ratio [OR] [95% confidence interval [CI]) and removal (Cox hazard ratio, hazard ratio [HR] [95% CI]). RESULTS: Of 924 patients, most had stage I to II (81%), p16+ (89%), node-positive (88%) disease. Median follow-up was 2.6 years (interquartile range 1.5-3.9). Most (91%) received radiation/chemoradiation, and 16% received TORS. G-tube rate was 27% (5% after TORS). G-tube risk was increased with chemoradiation (OR 2.78 [1.87-4.22]) and decreased with TORS (OR 0.31 [0.15-0.57]) and PSSHN-Diet score ≥60 (OR 0.26 [0.15-0.45]). G-tube removal probability over time was lower for T3 to T4 tumors (HR 0.52 [0.38-0.71]) and higher for PSSHN-Diet score ≥60 (HR 1.65 [1.03-2.66]). CONCLUSIONS: In this modern cohort of patients treated for OPC, 27% received G-tubes-50% less than institutional rates 10 years ago. Patients with preserved baseline swallowing and/or those eligible for TORS may have lower G-tube risk and duration.

Lacrimal Gland Adenocarcinoma Clinicopathologic Features and Outcomes Compared With Those of Lacrimal Gland Adenoid Cystic Carcinoma.

PURPOSE: Lacrimal gland (LG) adenocarcinomas (ACs) are rare, with limited data. We compared clinicopathologic features and local recurrence, distant metastasis, and survival rates between LG AC and LG adenoid cystic carcinoma (ACC). METHODS: The records of LG AC patients treated from 2008 to 2022 and LG ACC patients treated from 1998 to 2022 at the same center were retrospectively reviewed. RESULTS: The study included 20 patients with AC; 10 de-novo AC, 10 ex-pleomorphic AC; and 51 ACC patients. The median age at diagnosis was 61 years for de-novo AC, 54 years for ex-pleomorphic AC, and 45 years for ACC. All groups had male predominance. The initial T category was T2 in 50% (5/10) of de-novo ACs; 60% (6/10) of ex-pleomorphic ACs; and 59% (30/51) of ACCs. Perineural invasion was present in 33% (5/15) of ACs and 90% (45/50) of ACCs ( p < 0.001). Of the 20 AC patients, 14 had eye-sparing surgery; 4 had orbital exenteration; and 2 had unresectable disease. All AC patients received postoperative radiotherapy and 15 (75%) received concurrent chemotherapy. Fourteen AC patients were tested for human growth factor receptor 2 expression, and 10 (71%) were human growth factor receptor 2 positive; 5 received human growth factor receptor 2-targeted therapy. AC and ACC had similar 5-year recurrence rates (20% and 33%, respectively, p = 0.31) and metastasis rates (20% and 34%, respectively, p = 0.30). de-novo AC, ex-pleomorphic AC, and ACC had similar 5-year disease-specific survival rates (80%, 79%, and 81%, respectively, p > 0.99). CONCLUSIONS: LG AC and ACC have similar baseline clinicopathologic features, except that perineural invasion is more common in ACC, and similar recurrence, metastasis, and survival rates. Human growth factor receptor 2-targeted therapy may be appropriate in some patients with LG AC.

Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy.

Background and Purpose: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck. Materials and Methods: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics. Results: Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits. Conclusions: An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits.

Orbital and periocular complications in patients with sinonasal tumours with orbital invasion.

AIMS: The purpose of this study was to determine the frequency and associated risk factors of orbital/periocular complications in patients with sinonasal tumour with orbital invasion managed with eye-sparing treatments. METHODS: A retrospective case series of patients with primary sinonasal tumour with orbital invasion from January 2008 to December 2018. Patient factors were compared between the following groups: (1)patients with orbital/periocular complications versus those who did not and (2) patients who needed secondary oculoplastic surgical procedures versus those who did not. RESULTS: Out of 80 patients, 48 had eye-sparing surgery, 8 had orbital exenteration and 24 were managed non-surgically. The most common histology was squamous cell carcinoma (n=28, 35%). Among the eye-sparing treatment group, 51/72 patients experienced one or more orbital/periocular complication(s), with motility deficit (N=26, 36%) being the most frequent. Factors associated with higher risk of complications included tumour involving the orbital floor (p=0.019), clinical disease stage III/IV (p=0.038), maxillectomy (p=0.004), resection of the orbital floor (p=0.027) and cigarette smoking (p=0.041). Tumour involving the orbital floor had an OR of 3.9 (95% CI 1.3 to 11.6, p=0.016) in predicting orbital/periocular complication. In the eye-sparing surgery group, the most frequent secondary oculoplastic procedures was dacryocystorhinostomy (n=6, 13%). The use of a free flap in reconstruction had an OR of 8.2 (95% CI 2.1 to 31.8, p=0.002) in predicting need for secondary oculoplastic surgery. CONCLUSION: Majority of patients with sinonasal tumours and secondary orbital invasion were managed with eye-sparing multidisciplinary treatments. Preservation of the eye can lead to reasonably good functional outcome despite expected orbital and periocular complications.

Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation.

Purpose/Objectives: The purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/Methods: Patterns of LRR for 61 patients treated consecutively between 2003 and 2014 who received post-operative IMRT reirradiation to ≥ 60 Gy for recurrent HNSCC were determined by 2 methods: 1) physician classification via visual comparison of post-radiotherapy imaging to reirradiation plans; and 2) using deformable image registration (DIR). Those without evaluable CT planning image data were excluded. All recurrences were verified by biopsy or radiological progression. Failures were defined as in-field, marginal, or out-of-field. Logistic regression analyses were performed to identify predictors for LRR. Results: A total of 55 patients were eligible for analysis and 23 (42 %) had documented LRR after reirradiation. Location of recurrent disease prior to salvage surgery (lymphatic vs. mucosal) was the most significant predictor of LRR after post-operative reirradiation with salvage rate of 67 % for lymphatic vs. 33 % for mucosal sites (p = 0.037). Physician classification of LRR yielded 14 (61 %) in-field failures, 3 (13 %) marginal failures, and 6 (26 %) out-of-field failures, while DIR yielded 10 (44 %) in-field failures, 4 (17 %) marginal failures, and 9 (39 %) out-of-field failures. Most failures (57 %) occurred within the original site of recurrence or first echelon lymphatic drainage. Of patients who had a free flap placed during salvage surgery, 56 % of failures occurred within 1 cm of the surgical flap. Conclusion: Our study highlights the role of DIR in enhancing the accuracy and consistency of POF analysis. Compared to traditional visual inspection, DIR reduces interobserver variability and provides more nuanced insights into dose-specific and spatial parameters of locoregional recurrences. Additionally, the study identifies the location of the initial recurrence as a key predictor of subsequent locoregional recurrence after salvage surgery and re-IMRT.