All Publications

  • Deep learning-based classification of primary bone tumors on radiographs: A preliminary study. EBioMedicine He, Y., Pan, I., Bao, B., Halsey, K., Chang, M., Liu, H., Peng, S., Sebro, R. A., Guan, J., Yi, T., Delworth, A. T., Eweje, F., States, L. J., Zhang, P. J., Zhang, Z., Wu, J., Peng, X., Bai, H. X. 2020; 62: 103121


    BACKGROUND: To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists.METHODS: A total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and pre-operative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests.FINDINGS: For benign vs. not benign, model achieved area under curve (AUC) of 0894 and 0877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0907 and 0916 on cross-validation and external testing, respectively. For three-way classification, model achieved 721% accuracy vs. 746% and 721% for the two subspecialists on cross-validation (p=003 and p=052, respectively). On external testing, model achieved 734% accuracy vs. 693%, 734%, 731%, 679%, and 634% for the two subspecialists and three junior radiologists (p=014, p=089, p=093, p=002, p<001 for radiologists 1-5, respectively).INTERPRETATION: Deep learning can classify primary bone tumors using conventional radiographs in a multi-institutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists.FUNDING: The project described was supported byRSNAResearch & Education Foundation, through grant number RSCH2004 to Harrison X. Bai.

    View details for DOI 10.1016/j.ebiom.2020.103121

    View details for PubMedID 33232868

  • Persistent detection of SARS-CoV-2 RNA in patients and healthcare workers with COVID-19. Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology Gombar, S., Chang, M., Hogan, C. A., Zehnder, J., Boyd, S., Pinsky, B. A., Shah, N. H. 2020; 129: 104477


    BACKGROUND: Current guidelines for returning health care workers (HCW) to service after a positive SARS-CoV-2 RT-PCR test and ceasing of transmission precautions for patients is based on two general strategies. A test-based strategy that requires negative respiratory RT-PCR tests obtained after the resolution of symptoms. Alternatively, due to the limited availability of testing, many sites employ a symptom-based strategy that recommends excluding HCW from the workforce and keeping patients on contact precautions until a fixed period of time has elapsed from symptom recovery. The underlying assumption of the symptom-based strategy is that waiting for a fixed period of time is a surrogate for negative RT-PCR testing, which itself is a surrogate for the absence of shedding infectious virus.OBJECTIVES: To better understand the appropriate length of symptom based return to work and contact precaution strategies.STUDY DESIGN: We performed an observational analysis of 150 patients and HCW that transitioned from RT-PCR SARS-CoV-2 positive to negative over the course of 2 months at a US academic medical center.RESULTS: We found that the average time to transition from RT-PCR positive to negative was 24 days after symptom onset and 10 % remained positive even 33 days after symptom onset. No difference was seen in HCW and patients.CONCLUSIONS: These findings suggest until definitive evidence of the length of infective viral shedding is obtained that the fixed length of time before returning to work or ceasing contract precautions be revised to over one-month.

    View details for DOI 10.1016/j.jcv.2020.104477

    View details for PubMedID 32505778

  • Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clinical cancer research : an official journal of the American Association for Cancer Research Xi, I. L., Zhao, Y., Wang, R., Chang, M., Purkayastha, S., Chang, K., Huang, R. Y., Silva, A. C., Vallieres, M., Habibollahi, P., Fan, Y., Zou, B., Gade, T. P., Zhang, P. J., Soulen, M. C., Zhang, Z., Bai, H. X., Stavropoulos, S. W. 2020; 26 (8): 1944–52


    PURPOSE: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging.EXPERIMENTAL DESIGN: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model.RESULTS: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770).CONCLUSIONS: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.

    View details for DOI 10.1158/1078-0432.CCR-19-0374

    View details for PubMedID 31937619

  • Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma. Journal of magnetic resonance imaging : JMRI Zhao, Y., Chang, M., Wang, R., Xi, I. L., Chang, K., Huang, R. Y., Vallieres, M., Habibollahi, P., Dagli, M. S., Palmer, M., Zhang, P. J., Silva, A. C., Yang, L., Soulen, M. C., Zhang, Z., Bai, H. X., Stavropoulos, S. W. 2020


    Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making.PURPOSE: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma.STUDY TYPE: Retrospective.POPULATION: In all, 376 patients with 430 renal cell carcinoma lesions from 2008-2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set.FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T/T2 -weighted and T1 contrast-enhanced sequences.ASSESSMENT: The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model.STATISTICAL TESTS: Mann-Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity.RESULTS: The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73-0.96), sensitivity of 0.89 (95% CI: 0.74-0.96), and specificity of 0.88 (95% CI: 0.73-0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73-0.90), sensitivity of 0.92 (95% CI: 0.84-0.97), and specificity of 0.78 (95% CI: 0.68-0.86) in the WHO/ISUP test set.DATA CONCLUSION: Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy.LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

    View details for DOI 10.1002/jmri.27153

    View details for PubMedID 32222054

Latest information on COVID-19