Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs?

BACKGROUND: The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making. METHODS: Pre-treatment magnetic resonance (MR) images of patients who underwent surgery between 2015 and 2019 with available WHO 2021 classification were used. The tumours were manually segmented on the T1w, T1-contrast enhanced, and T2w images using 3D Slicer. One hundred Pyradiomic features were extracted from each MR sequence. Models were built to classify (1) somatotroph and gonadotroph PitNETs and (2) high- and low-risk subtypes of non-functioning PitNETs. Feature were selected independently from the MR sequences and multi-sequence (combining data from more than one MR sequence) using Boruta and Pearson correlation. Support vector machine (SVM), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) were the classifiers used. Data imbalance was addressed using the Synthetic Minority Oversampling TEchnique (SMOTE). Performance of the models were evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 222 PitNET patients (train, n = 149; test, n = 73) were enrolled in this retrospective study. Multi-sequence-based LR model discriminated best between somatotroph and gonadotroph PitNETs, with a test AUC of 0.84, accuracy of 0.74, specificity of 0.81, and sensitivity of 0.70. Multi-sequence-based MLP model perfomed best for the high- and low-risk non-functioning PitNETs, achieving a test AUC of 0.76, accuracy of 0.67, specificity of 0.72, and sensitivity of 0.66. CONCLUSIONS: Utilizing pre-treatment MRI and radiomics holds promise for distinguishing high-risk from low-risk non-functioning PitNETs based on the latest WHO classification. This could assist neurosurgeons in making critical decisions regarding surgery or alternative management strategies for PitNETs after further clinical validation.

UDHAVI Community Support During India's Second COVID-19 Wave: A Descriptive Study on a Tertiary Care Center's Pandemic Response Helpline.

BACKGROUND: In April 2021, during the peak of the second wave of the COVID-19 pandemic in India, hospitals overflowed with COVID-19 patients, and people hesitated to seek necessary care due to fear of contracting the disease. The UDHAVI helpline was set up by a tertiary care hospital in Vellore with the help of district administration, nongovernmental organizations, and various supporting agencies to provide general information, medical advice, counseling, and logistics support to the community. METHODS: This is a retrospective study of all the phone calls made to the UDHAVI helpline between mid-May and mid-June 2021 during the second wave of the COVID-19 pandemic. The calls were electronically captured as part of the process, and the information was subsequently retrieved and analyzed. RESULTS: In all, 677 calls were received. The lines for general information, medical advice, counseling, and logistics support received 168 (25%), 377 (56%), 15 (2%), and 117 (17%) calls, respectively. Home care kits, oxygen concentrators, and food were delivered by volunteers from local nongovernmental organizations and hospitals. CONCLUSION: We believe the details of our experience would be useful in the preparedness and mobilization of resources in the event of any public health emergency. As a result of this initiative, we propose an integrated partnership model for emergency response to any pandemic situation.

Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization.

Background and purpose: Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulate small and multi-institutional studies and study the responsiveness of the models to feature selection, machine learning algorithms, centre-effect harmonization and increased dataset sizes. Materials and methods: 562 patients histologically confirmed and treated for locally advanced head-and-neck cancer (LA-HNC) from two public and two private datasets; one private dataset exclusively reserved for validation. Clinical contours of primary tumours were not recontoured and were used for Pyradiomics based feature extraction. ComBat harmonization was applied, and LASSO-Logistic Regression (LR) and Support Vector Machine (SVM) models were built. 95% confidence interval (CI) of 1000 bootstrapped area-under-the-Receiver-operating-curves (AUC) provided predictive performance. Responsiveness of the models' performance to the choice of feature selection methods, ComBat harmonization, machine learning classifier, single and pooled data was evaluated. Results: LASSO and SelectKBest selected 14 and 16 features, respectively; three were overlapping. Without ComBat, the LR and SVM models for three institutional data showed AUCs (CI) of 0.513 (0.481-0.559) and 0.632 (0.586-0.665), respectively. Performances following ComBat revealed AUCs of 0.559 (0.536-0.590) and 0.662 (0.606-0.690), respectively. Compared to single cohort AUCs (0.562-0.629), SVM models from pooled data performed significantly better at AUC = 0.680. Conclusions: Multi-institutional retrospective data accentuates the existing variabilities that affect radiomics. Carefully designed prospective, multi-institutional studies and data sharing are necessary for clinically relevant head-and-neck cancer prognostication models.

Utility of Mid-treatment DWI in Selecting Pathological Responders to Neoadjuvant Chemoradiotherapy in Locally Advanced Esophageal Cancer.

PURPOSE: Pathological complete response correlates with better clinical outcomes in locally advanced esophageal cancer (LA-EC). However, there is lack of prognostic markers to identify patients in the current setting of neoadjuvant chemoradiotherapy (NACRT) followed by surgery. This study evaluates the utility of mid-treatment diffusion-weighted imaging (DWI) in identifying pathological responders of NACRT. METHODS: Twenty-four patients with LA-EC on NACRT were prospectively recruited and underwent three MRI (baseline, mid-treatment, end-of-RT) scans. DWI-derived apparent diffusion coefficient (ADC) mean and minimum were used as a surrogate to evaluate the treatment response, and its correlation to pathological response was assessed. RESULTS: Mid-treatment ADC mean was significantly higher among patients with pathological response compared to non-responders (p = 0.011). ADC difference (ΔADC) between baseline and mid-treatment correlated with tumor response (p = 0.007). ADC at other time points did not correlate to pathological response. CONCLUSION: In this study, mid-treatment ADC values show potential to be a surrogate for tumor response in NACRT. However, larger trials are required to establish DW-MRI as a definite biomarker for tumor response.

Radiation and immune checkpoint inhibitor-mediated pneumonitis risk stratification in patients with locally advanced non-small cell lung cancer: role of functional lung radiomics?

BACKGROUND: Patients undergoing chemoradiation and immune checkpoint inhibitor (ICI) therapy for locally advanced non-small cell lung cancer (NSCLC) experience pulmonary toxicity at higher rates than historical reports. Identifying biomarkers beyond conventional clinical factors and radiation dosimetry is especially relevant in the modern cancer immunotherapy era. We investigated the role of novel functional lung radiomics, relative to functional lung dosimetry and clinical characteristics, for pneumonitis risk stratification in locally advanced NSCLC. METHODS: Patients with locally advanced NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238). All received concurrent chemoradiation using functional lung avoidance planning, while approximately half received consolidation durvalumab ICI. Within tumour-subtracted lung regions, 110 radiomics features (size, shape, intensity, texture) were extracted on pre-treatment [99mTc]MAA SPECT/CT perfusion images using fixed-bin-width discretization. The performance of functional lung radiomics for pneumonitis (CTCAE v4 grade 2 or higher) risk stratification was benchmarked against previously reported lung dosimetric parameters and clinical risk factors. Multivariate least absolute shrinkage and selection operator Cox models of time-varying pneumonitis risk were constructed, and prediction performance was evaluated using optimism-adjusted concordance index (c-index) with 95% confidence interval reporting throughout. RESULTS: Thirty-nine patients were included in the study and pneumonitis occurred in 16/39 (41%) patients. Among clinical characteristics and anatomic/functional lung dosimetry variables, only the presence of baseline chronic obstructive pulmonary disease (COPD) was significantly associated with the development of pneumonitis (HR 4.59 [1.69-12.49]) and served as the primary prediction benchmark model (c-index 0.69 [0.59-0.80]). Discrimination of time-varying pneumonitis risk was numerically higher when combining COPD with perfused lung radiomics size (c-index 0.77 [0.65-0.88]) or shape feature classes (c-index 0.79 [0.66-0.91]) but did not reach statistical significance compared to benchmark models (p > 0.26). COPD was associated with perfused lung radiomics size features, including patients with larger lung volumes (AUC 0.75 [0.59-0.91]). Perfused lung radiomic texture features were correlated with lung volume (adj R2 = 0.84-1.00), representing surrogates rather than independent predictors of pneumonitis risk. CONCLUSIONS: In patients undergoing chemoradiation with functional lung avoidance therapy and optional consolidative immune checkpoint inhibitor therapy for locally advanced NSCLC, the strongest predictor of pneumonitis was the presence of baseline chronic obstructive pulmonary disease. Results from this novel functional lung radiomics exploratory study can inform future validation studies to refine pneumonitis risk models following combinations of radiation and immunotherapy. Our results support functional lung radiomics as surrogates of COPD for non-invasive monitoring during and after treatment. Further study of clinical, dosimetric, and radiomic feature combinations for radiation and immune-mediated pneumonitis risk stratification in a larger patient population is warranted.

Feasibility, safety and oncological outcomes of minimally invasive oesophagectomy following neoadjuvant chemoradiotherapy for oesophageal squamous cell carcinoma - Experience from a tertiary care centre.

Background: Neoadjuvant chemoradiotherapy (nCRT) has improved the resectability and survival of operable oesophageal squamous cell carcinoma (OSCC). We aimed to study if nCRT for OSCC makes minimally invasive oesophagectomy (MIO) technically more challenging and if the peri-operative and oncological outcomes are acceptable for MIO following nCRT. Materials and Methods: A retrospective review of patients with OSCC (cT1-2N1-2, cT3-4aN0-2) treated with nCRT and MIO between 2013 and 2019 was performed. The operative details including the technical difficulty in tumour dissection and lymphadenectomy, the post-operative complications and oncological outcomes were studied. Results: Seventy-five patients (male:female - 50:25; mean [range] age - 55.49 ± 8.43 [22-72] years; stage II - 34.7%; stage III - 37.3%; stage IVA - 28.0%) were enrolled. The concurrent chemotherapy course was completed by 25.3% of patients and the most common reason limiting the completion of chemotherapy was neutropaenia (66.0%). A thoraco-laparoscopic (n = 60) or hybrid (n = 15) McKeown's oesophagectomy with a two-field lymphadenectomy was performed. The increased surgical difficulty was reported in 41 (54.7%) patients, particularly for mid-thoracic tumours and tumours exhibiting incomplete response. The 30-day overall and major complication rate was 48.0% and 20.0%, respectively, and there was no mortality. The rate of R0 resection, pathological complete response and median lymph nodal yield were 93.3%, 48% and 8 (range: 1-25), respectively. The mean overall survival (OS) was 62.2 months (95% confidence interval [CI]: 52.6-71.8) and recurrence-free survival (RFS) was 53.5 months (95% CI: 43.5-63.5). The 1-, 2- and 3-year OS and RFS were 89.5%, 78.8% and 64.4% and 71.1%, 61.3% and 56.6%, respectively. Conclusion: Minimally invasive McKeown's oesophagectomy is feasible and safe in patients with OSCC receiving nCRT. The radiation component of nCRT increases the degree of operative difficulty, especially in relation to the supracarinal dissection and lymphadenectomy. However, this drawback did not adversely affect the short-term surgical or the long-term oncological outcomes.

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers.

CONTEXT: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). AIMS: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. SETTINGS AND DESIGN: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. SUBJECTS AND METHODS: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). RESULTS: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51-0.54). CONCLUSIONS: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.

Patterns of Recurrence in Locally Advanced Resectable Oesophageal Carcinoma: Retrospective Review from a Tertiary Cancer Centre in South India.

PURPOSE: The study aims to analyse patterns of recurrence following neoadjuvant treatment and surgery in carcinoma oesophagus with an intent to postulate optimal nodal radiation. METHODOLOGY: A retrospective review of patients who presented to our centre within a 5-year period (2014-2018), with recurrence following sequential neoadjuvant treatment and radical surgery, was conducted in this single-institution study. The patterns of recurrence and duration of disease-free survival were analysed. RESULTS: Twenty-one patients (14 men, 7 women) presented with recurrence, of which 13, 7, and 1 patient(s) had received NACT, NACTRT, or both, respectively. Six patients who did not receive neoadjuvant radiotherapy received adjuvant RT. Among the 10 patients who had nodal recurrence after RT (either neoadjuvant or adjuvant), 6 and 4 patients had in-field and out-of-field nodal recurrences, respectively-the latter were equally distributed within 5 cm and outside 5 cm of the PTV margin. CONCLUSION: Among the patients who presented with recurrence, more than half had not received neoadjuvant RT (treated in the 'pre-CROSS era' or due to long-segment disease), reasserting the therapeutic superiority of NACTRT. Increased regularity of recurrences in the draining nodal region was not noted in this study, but large-scale, prospective, randomised head-to-head comparative trials to determine optimal nodal irradiation in carcinoma oesophagus are required.

Scanning Beam Proton Therapy versus Photon IMRT for Stage III Lung Cancer: Comparison of Dosimetry, Toxicity, and Outcomes.

PURPOSE: There are limited clinical data on scanning-beam proton therapy (SPT) in treating locally advanced lung cancer, as most published studies have used passive-scatter technology. There is increasing interest in whether the dosimetric advantages of SPT compared with photon therapy can translate into superior clinical outcomes. We present our experience of SPT and photon intensity modulated radiation therapy (IMRT) with clinical dosimetry and outcomes in patients with stage III lung cancer. METHODS AND MATERIALS: Patients with stage III lung cancer treated at our center between 2013 and May 2018 were identified in compliance with our institutional review board (64 patients = 34 SPT + 30 IMRT). Most proton patients were treated with pencil beam scanning (28 of 34), and 6 of 34 were treated with uniform scanning. Fisher exact test, χ2 test, and Mann-Whitney test were used to compare groups. All tests were 2-sided. RESULTS: Patient characteristics were similar between the IMRT and SPT patients, except for worse lung function in the IMRT group. Mean dose to lung, heart, and esophagus was lower in the SPT group, with most benefit in the low-dose region (lungs, 9.7 Gy vs 15.7 Gy for SPT vs IMRT, respectively [P = .004]; heart, 7 Gy vs 14 Gy [P = .001]; esophagus, 28.2 Gy vs 30.9 Gy [P = .023]). Esophagitis and dermatitis grades were not different between the 2 groups. Grade 2+ pneumonitis was 21% in the SPT group and 40% in the IMRT group (P = .107). Changes in blood counts were not different between the 2 groups. Overall survival and progression-free survival were not different between SPT and IMRT (median overall survival, 41.6 vs 30.7 months, respectively [P = .52]; median progression-free survival, 19.5 vs 14.6 months [P = .50]). CONCLUSIONS: We report our experience with SPT and IMRT in stage III lung cancer. Our cohort of patients treated with SPT had lower doses to normal organs (lungs, heart, esophagus) than our IMRT cohort. There was no statistically significant difference in toxicity rates or survival, although there may have been a trend toward lower rates of pneumonitis.

Clinical Monte Carlo versus Pencil Beam Treatment Planning in Nasopharyngeal Patients Receiving IMPT.

PURPOSE: Pencil beam (PB) analytical algorithms have been the standard of care for proton therapy dose calculations. The introduction of Monte Carlo (MC) algorithms may provide more robust and accurate planning and can improve therapeutic benefit. We conducted a dosimetric analysis to quantify the differences between MC and PB algorithms in the clinical setting of dose-painted nasopharyngeal cancer intensity-modulated proton radiotherapy. PATIENTS AND METHODS: Plans of 14 patients treated with PB analytical algorithm optimized and calculated (PBPB) were retrospectively analyzed. The PBPB plans were recalculated using MC to generate PBMC plans and, finally, reoptimized and recalculated with MC to generate MCMC plans. The plans were compared across several dosimetric endpoints and correlated with documented toxicity. Robustness of the planning scenarios (PBPB, PBMC, MCMC) in the presence of setup and range uncertainties was compared. RESULTS: A median decrease of up to 5 Gy (P < .05) was observed in coverage of planning target volume high-risk, intermediate-risk, and low-risk volumes when PB plans were recalculated using the MC algorithm. This loss in coverage was regained by reoptimizing with MC, albeit with a slightly higher dose to normal tissues but within the standard tolerance limits. The robustness of both PB and MC plans remained similar in the presence of setup and range uncertainties. The MC-calculated mean dose to the oral avoidance structure, along with changes in global maximum dose between PB and MC dosimetry, may be associated with acute toxicity-related events. CONCLUSION: Retrospective analyses of plan dosimetry quantified a loss of coverage with PB that could be recovered under MC optimization. MC optimization should be performed for the complex dosimetry in patients with nasopharyngeal carcinoma before plan acceptance and should also be used in correlative studies of proton dosimetry with clinical endpoints.

Comparison of regional lung perfusion response on longitudinal MAA SPECT/CT in lung cancer patients treated with and without functional tissue-avoidance radiation therapy.

OBJECTIVE: The effect of functional lung avoidance planning on radiation dose-dependent changes in regional lung perfusion is unknown. We characterized dose-perfusion response on longitudinal perfusion single photon emission computed tomography (SPECT)/CT in two cohorts of lung cancer patients treated with and without functional lung avoidance techniques. METHODS: The study included 28 primary lung cancer patients: 20 from interventional (NCT02773238) (FLARE-RT) and eight from observational (NCT01982123) (LUNG-RT) clinical trials. FLARE-RT treatment plans included perfused lung dose constraints while LUNG-RT plans adhered to clinical standards. Pre- and 3 month post-treatment macro-aggregated albumin (MAA) SPECT/CT scans were rigidly co-registered to planning four-dimensional CT scans. Tumour-subtracted lung dose was converted to EQD2 and sorted into 5 Gy bins. Mean dose and percent change between pre/post-RT MAA-SPECT uptake (%ΔPERF), normalized to total tumour-subtracted lung uptake, were calculated in each binned dose region. Perfusion frequency histograms of pre/post-RT MAA-SPECT were analyzed. Dose-response data were parameterized by sigmoid logistic functions to estimate maximum perfusion increase (%ΔPERFmaxincrease), maximum perfusion decrease (%ΔPERFmaxdecrease), dose midpoint (Dmid), and dose-response slope (k). RESULTS: Differences in MAA perfusion frequency distribution shape between time points were observed in 11/20 (55%) FLARE-RT and 2/8 (25%) LUNG-RT patients (p < 0.05). FLARE-RT dose response was characterized by >10% perfusion increase in the 0-5 Gy dose bin for 8/20 patients (%ΔPERFmaxincrease = 10-40%), which was not observed in any LUNG-RT patients (p = 0.03). The dose midpoint Dmid at which relative perfusion declined by 50% trended higher in FLARE-RT compared to LUNG-RT cohorts (35 GyEQD2 vs 21 GyEQD2, p = 0.09), while the dose-response slope k was similar between FLARE-RT and LUNG-RT cohorts (3.1-3.2, p = 0.86). CONCLUSION: Functional lung avoidance planning may promote increased post-treatment perfusion in low dose regions for select patients, though inter-patient variability remains high in unbalanced cohorts. These preliminary findings form testable hypotheses that warrant subsequent validation in larger cohorts within randomized or case-matched control investigations. ADVANCES IN KNOWLEDGE: This novel preliminary study reports differences in dose-response relationships between patients receiving functional lung avoidance radiation therapy (FLARE-RT) and those receiving conventionally planned radiation therapy (LUNG-RT). Following further validation and testing of these effects in larger patient populations, individualized estimation of regional lung perfusion dose-response may help refine future risk-adaptive strategies to minimize lung function deficits and toxicity incidence.

A phase 2 randomized controlled trial of oral resistant starch supplements in the prevention of acute radiation proctitis in patients treated for cervical cancer.

BACKGROUND: Radiation induced proctitis is frequently encountered during the radiation therapy of cervical and prostate cancers that causes pain and occasionally with bleeding and may affect the continuity of radiation therapy. AIMS AND OBJECTIVES: The purpose of the study is to look at the benefit of administration of an oral prebiotic amylase resistant starch in reducing the incidence of acute radiation proctitis, a distressing symptom in patients receiving radiation therapy for cancer of the cervix. MATERIAL AND METHODS: The study was conducted between 2011 and 2014 in 104 patients receiving radical chemo-radiotherapy for carcinoma cervix. Patients were randomized in to two arms, one receiving 30 gm of resistant starch and the other digestible starch on a daily basis throughout the course of the external radiotherapy. All patients received standard 4-field box radiation portals, 50 Gy in 25 fractions with 4 cycles of weekly concurrent Cisplatin. At completion of external beam radiotherapy, all patients underwent LDR/HDR brachytherapy. The study was double blinded and allocation was concealed from the investigators. The investigator recorded the radiotherapy related toxicity of the patients according to CTC V 3.0. The incidence and severity of grade 2-4 diarrhoea and proctitis were documented on a weekly basis and compared across the two groups and analyzed. Stool short chain fatty acid concentrations were measured at baseline at 2nd and 4th week and after 6 weeks of completion of radiotherapy in both study placebo arms and reported. The pattern of microbiota in the stool were also estimated in all patients at 4 time points. Two patients who progressed during therapy were not included in the analyses and two patients discontinued the intervention. A per protocol analyses was done. RESULTS: At analysis there were 50 patients in each arm. The severity of clinical proctitis was found to be similar in both groups of patients with 12.2 % of patients experiencing toxicity of grade 2 and above in digestible starch group versus 14.6% in the resistant starch group. Functional proctitis was similarly graded and it was found that 16.3 % patients in digestible starch group experienced toxicity against 10.2 % patients in the resistant starch group. This difference was seen at 4th week and continued in the subsequent weeks till the end of radiation. Both groups had similar reported toxicity at 6 weeks post intervention and similar incidence of grade 2 and above diarrhea. The resistant starch group was found to have 8% incidence as compared to 2% in the other group at the 5th and 6th week. The short chain fatty acid concentrations were not significantly different in the groups at any point. CONCLUSION: The study did not demonstrate a significant benefit in administering resistant starch over and above normal diet to patients receiving pelvic radiotherapy. The reasons may be attributed to concurrent use of chemotherapy and decrease in intestinal probiotics. The use of digestible starch in the control arm may have contributed to lower incidence of the toxicity endpoints as well.