PURPOSE A COVID-19 lockdown in India posed significant challenges to the continuation of radiotherapy (RT) and systemic therapy services. Although several COVID-19 service guidelines have been promulgated, implementation data are yet unavailable. We performed a comprehensive audit of the implementation of services in a clinical oncology department. METHODS A departmental protocol of priority-based treatment guidance was developed, and a departmental staff rotation policy was implemented. Data were collected for the period of lockdown on outpatient visits, starting, and delivery of RT and systemic therapy. Adherence to protocol was audited, and factors affecting change from pre-COVID standards analyzed by multivariate logistic regression. RESULTS Outpatient consults dropped by 58%. Planned RT starts were implemented in 90%, 100%, 92%, 90%, and 75% of priority level 1-5 patients. Although 17% had a deferred start, the median time to start of adjuvant RT and overall treatment times were maintained. Concurrent chemotherapy was administered in 89% of those eligible. Systemic therapy was administered to 84.5% of planned patients. However, 33% and 57% of curative and palliative patients had modifications in cycle duration or deferrals. The patient’s inability to come was the most common reason for RT or ST deviation. Factors independently associated with a change from pre-COVID practice was priority-level allocation for RT and age and palliative intent for systemic therapy. CONCLUSION Despite significant access limitations, a planned priority-based system of delivery of treatment could be implemented.

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Background: The comprehensive healthcare approach including prophylactic guidance and motivation by the primary healthcare professionals towards oral and maxillofacial diseases such as post-treatment endodontic disease (PTED) plays a significant role in diagnosing and managing the condition. Especially in the developing countries like India where the hygiene practices are severely compromised, the primary healthcare professional plays an upfront role. Objectives: The present study was conducted to assess the clinical and radiographic characteristics of PTED by primary healthcare professional. Materials and Methods: The cross-sectional study was conducted in a dental hospital in Kutch, Gujarat, India. In the present study, out of a total of 755, 96 patients were diagnosed with PTED, met the inclusion criteria, and were enrolled for the study. After performing intraoral and extraoral examination, intraoral periapical radiographs were taken of the concerned teeth. Under dark room conditions, radiographs were examined using dentsply light box and magnifying glass by healthcare professionals. Results: Out of 755 patients, 96 (12.71%) patients were enrolled in the study with 98 concerned teeth. The most common teeth diagnosed with PTED were maxillary molars with 25.51% (21) individuals. Well-defined radiolucent lesions were seen in 62.24% (61) individuals. Voids in both coronal and apical region were seen in majority (38.77%) of patients. The length of root-end fillings with respect to the radiographic apex was satisfactory in 44.89% (44) individuals. The present study showed strong correlation between sinus formation and presence of periapical lesion with P value of 0.0219*. Conclusion: The proper guidance and preventive care by primary healthcare professionals leads to the relatively less prevalence of post-treatment endodontic disease in Indian population. The present study further suggests the higher substandard quality of root-end fillings of endodontically treated teeth.

PURPOSE/OBJECTIVE(S) A low-cost, prior knowledge-based individualized dose-constraint generator for organs-at-risk has been developed for prostate cancer radiation therapy (RT) planning. In this study, we aimed to prospectively evaluate the feasibility and improvements in organs-at-risk (OAR) doses in prostate cancer RT planning using this tool served on a web application. MATERIALS AND METHODS: A set of previously treated prostate cancer cases planned and treated with generic constraints were prospectively replanned using individualized dose constraints derived from a library of cases with similar volumes of target, OAR, and overlap regions and served on the web-based application. The goal was to assess the reduction in mean dose, specified dose volumes (V59Gy, V56Gy, V53Gy, V47Gy, and V40Gy), and generalized equivalent uniform dose (gEUD) to the rectum and bladder. Planners and assessors were blinded to the initial achieved doses and penalties. Sample size estimation was based on improvement in V53Gy for the rectum and bladder with a paired evaluation. RESULTS: Twenty-four patients were prospectively replanned. All the plans had a PTV D95 of at least 97% of the prescribed dose. The individualized OAR constraints could be met for 87.5% of patients for all dose levels. The mean dose, V59Gy, V53Gy, and V47Gy for the bladder was reduced by 7.5Gy, 1.12%, 5.51%, and 10.53% respectively. Similarly for the rectum, the mean dose, V59Gy, V53Gy, V47Gy and was reduced by 5.5Gy, 4.34%, 6.97%, and 11.61% respectively. All dose reductions were statistically significant. The gEUD of the bladder was reduced by 2.47Gy (p <0.001) and the rectum by 3.21Gy (p <0.001). CONCLUSION: Treatment planning based on individualized dose constraints served on a web application is feasible and leads to improvement at clinically important dose volumes in prostate cancer RT planning. This application can be served publicly for improvements in RT plan quality.

e18025 Background: The current approach to neck treatment in clinical T1-2 oral cancers is to offer elective nodal dissection to all patients, despite the fact that the majority of patients are pathologically node negative. This is due to the poor predictive ability of clinico-radiological assessment and subsequently poorer survival in those in whom neck dissection is omitted based on this. A robust prediction model for pathological nodal status may allow individualized decisions for neck dissection. Our aim was to develop a multiparameter prediction model to identify pathological node-negative status using machine learning. Methods: We identified 497 patients with cT1-2 oral cancer from a single institutional database from 2011-2018 who underwent primary resection and neck dissection. We compared the sensitivity, positive predictive value and accuracy of prediction of pathologically negative neck from clinico-radiological staging alone vs. a model created from multiple parameters including clinical features (clinico-radiological nodal status, ages, sex, subsite of primary lesion) and pathological features of the resected primary tumor (maximum dimension, depth of invasion, lymphovascular invasion, perineural invasion, grade and margins of resection). The multiparameter model was built from a training dataset of the first 400 patients using an ensemble of logistic regression, random forests and support vector machines. A cohort of 97 patients was used for independent validation. Results: In this cohort 232 (47%) were clinico-radiologically node negative, while 307(62%) were pathologically node negative. The sensitivity, positive predictive value and accuracy of the clinico-radiologically assigned nodal status was 56%, 74% and 61%, while that of the multiparameter machine learning model was 87%, 89% and 89% respectively. The area under curve (AUC) of the clinico-radiological prediction was 0.62 whereas that of the multiparameter predictive model was 0.91. In the validation dataset, 58/62 pathologically node negative patients were predicted correctly by the model. The accuracy of the model on the external validation dataset was 82%. Conclusions: The performance of the multiparameter predictive model was considerably superior to clinico-radiological neck staging for prediction of pathological node negative neck. This could be validated on an independent dataset. This could be considered for prospective clinical evaluation of individualized neck dissection.