TREATMENT OUTCOME AND TOXICITY OF HYPOFRACTIONATED RADIOTHERAPY WITH CONCOMITANT CHEMOTHERAPY VERSUS CONVENTIONAL FRACTIONATED CONCOMITANT CHEMORADIATION IN LOCALLY ADVANCED HEAD-AND-NECK CARCINOMA: A COMPARATIVE STUDY

Objectives: In our study, radiation of a higher dose per fraction (2.75 Gy/fraction, total dose of 55 Gy/20 fractions/4 weeks) with concomitant chemotherapy was compared with conventional chemoradiation (2 Gy/fraction, a total dose of 66 Gy/33 fractions/6 and half weeks, with concomitant chemotherapy), in patients of locally advanced squamous cell carcinomas of head and neck in terms of efficacy and toxicities. Methods: A total of 75 patients registered at the Department of Radiotherapy, NRS Medical College and Hospital, Kolkata, were allotted in two arms chronologically in a 1:1 ratio. Arm A – Patients received hypofractionated radiotherapy, 55 Gy/20 fractions in 4 weeks with concomitant weekly cisplatin (40 mg/m2). Arm B – Patients received conventional radiotherapy, 66 Gy/33 fractions in 6½ weeks with concomitant weekly cisplatin (40 mg/m2). Results: Both in terms of efficacy and toxicities, the hypofractionation arm was comparable to the conventional arm, and no statistically significant difference was present between the arms. For the study arm, complete response was 56.6%, partial response was 36.6%, and for control arm, complete response 50% and partial response 37.5% (p=0.750). In terms of acute toxicities and late dysphagia, both the arms were almost similar. Conclusion: The hypofractionated regimen was associated with tolerable acute and late toxicities and satisfactory local control. Considering the patient load, the overall treatment time, and the cost of hospital stay, this hypofractionated regimen is a good treatment option in our low-resource setup

Marjolin’s ulcer – epidemiology and the pattern of care: Experience from a tertiary cancer care center

Background: Marjolin’s ulcer (MU) reflects the malignant transformation of scar tissue or chronic inflammatory skin lesions. Due to the low incidence, only a few case reports and case series were reported in the literature to date. Aims and Objectives: In our retrospective analysis, we report our experience regarding MU over the past 16 years with a significant range of latent period, histology, and as well as underlying skin conditions, from Kolkata, India. Materials and Methods: This was a single institutional retrospective study carried out in the department of radiotherapy at the tertiary cancer, Kolkata. We focus mainly on data pertaining to the type of injury, the time gap between the initial injury and development of MU, histology, clinical stage, and treatment received, along with demographic details. Results: A total of 39 patients with histopathologically proved MUs were included according to the inclusion and exclusion criteria. The median age of patients at the time of development of MU was 52 years and with a range of 32–71 years. Out of 39 patients who participated in this study, 29 were male (74%) and ten were female (26%), 31 patients have localized disease (80%) and eight patients have metastatic disease (20%). The most common histology is squamous cell carcinoma and the least common histology was spindle cell sarcoma. The majority of the patients were treated with curative intention and a small proportion of patients were offered palliative therapy. Conclusion: Ulcers refractory to basic wound care, chronic in nature, and recent change of character of long-standing scar or wound, diagnosis of MU should be ruled out by biopsy. The management of MU should be supervised by a multidisciplinary tumor board and in the areas of epidemiology and treatment, more research is needed

CVAD - An unsupervised image anomaly detector

Detecting out-of-distribution samples for image applications plays an important role in safeguarding the reliability of machine learning model deployment. In this article, we developed a software tool to support our OOD detector CVAD - a self-supervised Cascade Variational autoencoder-based Anomaly Detector , which can be easily applied to various image applications without any assumptions. The corresponding open-source software is published for better public research and tool usage

Margin-Aware Intra-Class Novelty Identification for Medical Images

Purpose: Existing anomaly detection methods focus on detecting interclass variations while medical image novelty identification is more challenging in the presence of intraclass variations. For example, a model trained with normal chest x-ray and common lung abnormalities is expected to discover and flag idiopathic pulmonary fibrosis, which is a rare lung disease and unseen during training. The nuances of intraclass variations and lack of relevant training data in medical image analysis pose great challenges for existing anomaly detection methods. Approach: We address the above challenges by proposing a hybrid model—transformation-based embedding learning for novelty detection (TEND), which combines the merits of classifier-based approach and AutoEncoder (AE)-based approach. Training TEND consists of two stages. In the first stage, we learn in-distribution embeddings with an AE via the unsupervised reconstruction. In the second stage, we learn a discriminative classifier to distinguish in-distribution data and the transformed counterparts. Additionally, we propose a margin-aware objective to pull in-distribution data in a hypersphere while pushing away the transformed data. Eventually, the weighted sum of class probability and the distance to margin constitutes the anomaly score. Results: Extensive experiments are performed on three public medical image datasets with the one-vs-rest setup (namely one class as in-distribution data and the left as intraclass out-of-distribution data) and the rest-vs-one setup. Additional experiments on generated intraclass out-of-distribution data with unused transformations are implemented on the datasets. The quantitative results show competitive performance as compared to the state-of-the-art approaches. Provided qualitative examples further demonstrate the effectiveness of TEND. Conclusion: Our anomaly detection model TEND can effectively identify the challenging intraclass out-of-distribution medical images in an unsupervised fashion. It can be applied to discover unseen medical image classes and serve as the abnormal data screening for downstream medical tasks. The corresponding code is available at https://github.com/XiaoyuanGuo/TEND_MedicalNoveltyDetection