Education & Certifications

  • Master of Science, University of Cambridge, Physics (2018)
  • Bachelor of Arts, University of Cambridge, Natural Sciences (2018)

All Publications

  • Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning. Cancers Moroianu, S. L., Bhattacharya, I., Seetharaman, A., Shao, W., Kunder, C. A., Sharma, A., Ghanouni, P., Fan, R. E., Sonn, G. A., Rusu, M. 2022; 14 (12)


    The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.

    View details for DOI 10.3390/cancers14122821

    View details for PubMedID 35740487

  • The acoustic resonant drag instability with a spectrum of grain sizes MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY Squire, J., Moroianu, S., Hopkins, P. F. 2022; 510 (1): 110-130
  • Intensity Normalization of Prostate MRIs using Conditional Generative Adversarial Networks for Cancer Detection DeSilvio, T., Moroianu, S., Bhattacharya, I., Seetharaman, A., Sonn, G., Rusu, M., Mazurowski, M. A., Drukker, K. SPIE-INT SOC OPTICAL ENGINEERING. 2021

    View details for DOI 10.1117/12.2582297

    View details for Web of Science ID 000672800100016

  • Detecting Invasive Breast Carcinoma on Dynamic Contrast-Enhanced MRI Moroianu, S. L., Rusu, M., Mazurowski, M. A., Drukker, K. SPIE-INT SOC OPTICAL ENGINEERING. 2021

    View details for DOI 10.1117/12.2580989

    View details for Web of Science ID 000672800100012