Publications by authors named "Shaurav Maulik"

3 Publications

  • Page 1 of 1

Prioritizing Delivery of Cancer Treatment During a COVID-19 Lockdown: The Experience of a Clinical Oncology Service in India.

JCO Glob Oncol 2021 01;7:99-107

Department of Radiation Oncology, Tata Medical Center, Kolkata, India.

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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January 2021

Ten years and counting: Survival in stage IV metastatic squamous cell carcinoma of anal canal following radical treatment.

J Cancer Res Ther 2020 Dec;16(Supplement):S227-S229

Department Radiation Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India.

A 60-year-old male ptient presented with a 2-month history of altered bowel habits and occasional bleeding per rectum. On evaluation, he was diagnosed with squamous cell carcinoma of the anal canal (SCCAC) with an isolated hepatic lesion in segment II estimated as 4.3 cm × 3.5 cm on ultrasound. Subsequent needle biopsy confirmed metastatic squamous cell carcinoma deposits. The final diagnosis was SCCAC, cT4N1M1 (Stage IV). The patient was offered radical intent treatment. As per institutional protocol, the patient received two cycles of induction cisplatin + 5fluorouracil (FU) followed by chemo-radiation. 5FU and mitomycin C was given concurrently with irradiation. The primary and metastatic sites were irradiated using 6 MV photons on helical tomotherapy using conventional fractionation. Fluorodeoxyglucose positron emission tomograph-computed tomography performed 4 months after treatment completion showed a complete metabolic and morphological response. As of the date of writing, the patient is alive and disease free, 10 years after treatment with no long term sequelae.
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December 2020

Prediction of survival outcome based on clinical features and pretreatment FDG-PET/CT for HNSCC patients.

Comput Methods Programs Biomed 2020 Oct 18;195:105669. Epub 2020 Jul 18.

Department of Computer Science & Engineering, Indian Institute Of Technology Kharagpur, Kharagpur, West Bengal, 721302, India. Electronic address:

Background And Objective: In this study, we have analysed pretreatment positron-emission tomography/ computed tomography (PET/CT) images of head and neck squamous cell carcinoma (HNSCC) patients. We have used a publicly available dataset for our analysis. The clinical features of the patient, PET quantitative parameters, and textural indices from pretreatment PET-CT images are selected for the study. The main objective of the study is to use classifiers to predict the outcome for HNSCC patients and compare the performance of the model with the conventional statistical model (CoxPH).

Methods: We have applied a 40% fixed SUV threshold method for tumour delineation. Clinical features of each patient are provided in the dataset, and other features are calculated using LIFEx software. For predicting the outcome, we have implemented three classifiers - Random Forest classifier, Gradient Boosted Decision tree (GBDT) and Decision tree classifier. We have trained each model using 93 data points and test the model performance using 39 data points. The best model - GBDT is chosen based on the performance metrics.

Results: It is observed that typically three features: MTV (Metabolic tumour Volume), primary tumour site and GLCM_correlation are significant for prediction of survival outcome. For testing cohort, GBDT achieves a balanced accuracy of 88%, where conventional statistical model reported a balanced accuracy of 81.5%.

Conclusions: The proposed classifier achieves higher accuracy than the state of the art technique. Using this classifier we can estimate the HNSCC patient's outcome, and depending upon the outcome treatment policy can be selected.
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October 2020