Bio

I received my B. Tech(Hons) with a major in Electronics and Electrical Communication Engineering and a minor in Computer Science and Engineering from Indian Institute of Technology (IIT), Kharagpur in 2010. In 2016, I obtained a Ph.D in Electrical and Computer Engineering at the University of Maryland, College Park under the guidance of Prof. Sennur Ulukus. From January to December 2017, I was a postdoctoral researcher in the Electrical Engineering department at Stanford University with Prof. Tsachy Weissman and Prof. Ayfer Ozgur. From January 2018, I joined the Gevaertlab at BMIR in the Stanford School of Medicine where I am currently pursuing research into the application of machine learning and deep learning to uncover the interplay between biomedical imaging and genomics, as they relate to cancer research.

Professional Education

  • Doctor of Philosophy, University of Maryland College Park (2016)
  • Bachelor of Technology, Indian Institute of Technology, Kharagpur (2010)

Contact

  • Academic
    pritamm@stanford.edu
    University - Scholar Department: Department of Medicine, Center for Biomedical Informatics Research Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5479

All Publications

  • Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans. JCO clinical cancer informatics Shah, R. P., Selby, H. M., Mukherjee, P., Verma, S., Xie, P., Xu, Q., Das, M., Malik, S., Gevaert, O., Napel, S. 2021; 5: 746-757

    Abstract

    PURPOSE: Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.MATERIALS AND METHODS: Computed tomography scans from a single institution were selected and resampled to 1 * 1 * 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance.RESULTS: A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B.CONCLUSION: A machine learning radiomics model may help differentiate SCLC from other lung lesions.

    View details for DOI 10.1200/CCI.21.00021

    View details for PubMedID 34264747

  • CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma. Radiology. Imaging cancer Mukherjee, P., Cintra, M., Huang, C., Zhou, M., Zhu, S., Colevas, A. D., Fischbein, N., Gevaert, O. 2020; 2 (3): e190039

    Abstract

    Purpose: To determine the performance of CT-based radiomic features for noninvasive prediction of histopathologic features of tumor grade, extracapsular spread, perineural invasion, lymphovascular invasion, and human papillomavirus status in head and neck squamous cell carcinoma (HNSCC).Materials and Methods: In this retrospective study, which was approved by the local institutional ethics committee, CT images and clinical data from patients with pathologically proven HNSCC from The Cancer Genome Atlas (n = 113) and an institutional test cohort (n = 71) were analyzed. A machine learning model was trained with 2131 extracted radiomic features to predict tumor histopathologic characteristics. In the model, principal component analysis was used for dimensionality reduction, and regularized regression was used for classification.Results: The trained radiomic model demonstrated moderate capability of predicting HNSCC features. In the training cohort and the test cohort, the model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.81) and 0.66 (95% CI: 0.45, 0.84), respectively, for tumor grade; a mean AUC of 0.64 (95% CI: 0.55, 0.62) and 0.70 (95% CI: 0.47, 0.89), respectively, for perineural invasion; a mean AUC of 0.69 (95% CI: 0.56, 0.81) and 0.65 (95% CI: 0.38, 0.87), respectively, for lymphovascular invasion; a mean AUC of 0.77 (95% CI: 0.65, 0.88) and 0.67 (95% CI: 0.15, 0.80), respectively, for extracapsular spread; and a mean AUC of 0.71 (95% CI: 0.29, 1.0) and 0.80 (95% CI: 0.65, 0.92), respectively, for human papillomavirus status.Conclusion: Radiomic CT models have the potential to predict characteristics typically identified on pathologic assessment of HNSCC.Supplemental material is available for this article.© RSNA, 2020.

    View details for DOI 10.1148/rycan.2020190039

    View details for PubMedID 32550599

  • A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets Nature Machine Intelligence Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., Gevaert, O. 2020; 2 (5): 274–282

    View details for DOI 10.1038/s42256-020-0173-6

  • A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data. Nature machine intelligence Mukherjee, P. n., Zhou, M. n., Lee, E. n., Schicht, A. n., Balagurunathan, Y. n., Napel, S. n., Gillies, R. n., Wong, S. n., Thieme, A. n., Leung, A. n., Gevaert, O. n. 2020; 2 (5): 274–82

    Abstract

    Lung cancer is the most common fatal malignancy in adults worldwide, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography (CT) is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of NSCLC patients. We trained and evaluated LungNet on four independent cohorts of NSCLC patients from four medical centers: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité - Universitätsmedizin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3, and 4, respectively. Further, the survival model can be used, via transfer learning, for classifying benign vs malignant nodules on the Lung Image Database Consortium (n = 1010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a noninvasive predictor for prognosis in NSCLC patients and can facilitate interpretation of CT images for lung cancer stratification and prognostication.

    View details for DOI 10.1038/s42256-020-0173-6

    View details for PubMedID 33791593

    View details for PubMedCentralID PMC8008967

  • Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Bulens, P., Couwenberg, A., Intven, M., Debucquoy, A., Vandecaveye, V., Van Cutsem, E., D'Hoore, A., Wolthuis, A., Mukherjee, P., Gevaert, O., Haustermans, K. 2019

    Abstract

    BACKGROUND: In well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection.MATERIALS AND METHODS: Models were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation.RESULTS: 21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70-0.95), 0.86 (95% CI 0.75-0.98) and 0.84 (95% CI 0.75-0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70-0.95) and 0.86 (95% CI 0.76-0.97). These models however did not outperform a previously established four-feature semantic model.CONCLUSION: Prediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment.

    View details for DOI 10.1016/j.radonc.2019.07.033

    View details for PubMedID 31431368

  • Secure Degrees of Freedom of the Multiple Access Wiretap Channel With Multiple Antennas IEEE TRANSACTIONS ON INFORMATION THEORY Mukherjee, P., Ulukus, S. 2018; 64 (3): 2093–2103

    View details for DOI 10.1109/TIT.2018.2793848

    View details for Web of Science ID 000425665200039

  • Distributed Statistical Estimation of High-Dimensional and Nonparametric Distributions Han, Y., Mukherjee, P., Ozgur, A., Weissman, T., IEEE IEEE. 2018: 506–10

    View details for Web of Science ID 000448139300102

  • Secrecy in MIMO Networks With No Eavesdropper CSIT Mukherjee, P., Ulukus, S. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2017: 4382–91

    View details for DOI 10.1109/TCOMM.2017.2705054

    View details for Web of Science ID 000413137400023

  • Secure Degrees of Freedom Region of the Two-User MISO Broadcast Channel With Alternating CSIT IEEE TRANSACTIONS ON INFORMATION THEORY Mukherjee, P., Tandon, R., Ulukus, S. 2017; 63 (6): 3823–53

    View details for DOI 10.1109/TIT.2017.2663427

    View details for Web of Science ID 000402058900030

  • Secure Degrees of Freedom of One-Hop Wireless Networks With No Eavesdropper CSIT Mukherjee, P., Xie, J., Ulukus, S. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2017: 1898–1922

    View details for DOI 10.1109/TIT.2016.2618725

    View details for Web of Science ID 000395822500033

  • Covert Bits Through Queues Mukherjee, P., Ulukus, S., IEEE IEEE. 2016: 626–30

    View details for Web of Science ID 000402623000097

  • Real Interference Alignment for the MIMO Multiple Access Wiretap Channel Mukherjee, P., Ulukus, S., IEEE IEEE. 2016

    View details for Web of Science ID 000390993202038

  • Real Interference Alignment for Vector Channels Mukherjee, P., Ulukus, S., IEEE IEEE. 2016: 1476–80

    View details for Web of Science ID 000390098701108

  • MIMO One Hop Networks with No Eve CSIT Mukherjee, P., Ulukus, S., IEEE IEEE. 2016: 894–901

    View details for Web of Science ID 000400601400127

  • Secure Degrees of Freedom of MIMO Rayleigh Block Fading Wiretap Channels With No CSI Anywhere IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Liu, T., Mukherjee, P., Ulukus, S., Lin, S., Hong, Y. 2015; 14 (5): 2655–69

    View details for DOI 10.1109/TWC.2015.2390234

    View details for Web of Science ID 000354468600024

  • Secure Degrees of Freedom of the Interference Channel with No Eavesdropper CSI Mukherjee, P., Ulukus, S., IEEE IEEE. 2015: 317–21

    View details for Web of Science ID 000380406900066

  • Secrecy for MISO Broadcast Channels via Alternating CSIT Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2015: 4157–62

    View details for Web of Science ID 000371708104062

  • Secrecy for MISO Broadcast Channels with Heterogeneous CSIT Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2015: 1966–70

    View details for Web of Science ID 000380904702004

  • Secure Degrees of Freedom of the Multiple Access Wiretap Channel with No Eavesdropper CSI Mukherjee, P., Ulukus, S., IEEE IEEE. 2015: 2311–15

    View details for Web of Science ID 000380904702073

  • MISO Broadcast Channels with Confidential Messages and Alternating CSIT Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2014: 216–20

    View details for Web of Science ID 000346496100043

  • Secure DoF of MIMO Rayleigh Block Fading Wiretap Channels with No CSI Anywhere Liu, T., Mukherjee, P., Ulukus, S., Lin, S., Hong, Y., Jamalipour, A., Deng, D. J. IEEE. 2014: 1959–64

    View details for Web of Science ID 000366666802018

  • Even Symmetric Parallel Linear Deterministic Interference Channels are Inseparable Mukherjee, P., Tandon, R., Ulukus, S., IEEE IEEE. 2013: 1106–13

    View details for Web of Science ID 000350802400152

  • Fading Wiretap Channel with No CSI Anywhere Mukherjee, P., Ulukus, S., IEEE IEEE. 2013: 1347–51

    View details for Web of Science ID 000348913401096

  • A SPT Treatment to the Realization of the Sign-LMS Based Adaptive Filters IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS Choudhary, S., Mukherjee, P., Chakraborty, M., Rath, S. 2012; 59 (9): 2025–33

    View details for DOI 10.1109/TCSI.2012.2185300

    View details for Web of Science ID 000308109600018

  • A SPT treatment to the Bit Serial Realization of the Sign-LMS based Adaptive Filter Choudhary, S., Mukherjee, P., Chakraborty, M., IEEE IEEE. 2010: 2678–81

    View details for Web of Science ID 000287216002224