APA (7th ed.) Citation

Lim, C. S., Abreu-Gomez, J., Thornhill, R., James, N., Al Kindi, A., Lim, A. S., & Schieda, N. (2021). Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdominal imaging, 46(12), 5647-5658. https://doi.org/10.1007/s00261-021-03235-0

Chicago Style (17th ed.) Citation

Lim, Christopher S., Jorge Abreu-Gomez, Rebecca Thornhill, Nick James, Ahmed Al Kindi, Andrew S. Lim, and Nicola Schieda. "Utility of Machine Learning of Apparent Diffusion Coefficient (ADC) and T2-weighted (T2W) Radiomic Features in PI-RADS Version 2.1 Category 3 Lesions to Predict Prostate Cancer Diagnosis." Abdominal Imaging 46, no. 12 (2021): 5647-5658. https://doi.org/10.1007/s00261-021-03235-0.

MLA (9th ed.) Citation

Lim, Christopher S., et al. "Utility of Machine Learning of Apparent Diffusion Coefficient (ADC) and T2-weighted (T2W) Radiomic Features in PI-RADS Version 2.1 Category 3 Lesions to Predict Prostate Cancer Diagnosis." Abdominal Imaging, vol. 46, no. 12, 2021, pp. 5647-5658, https://doi.org/10.1007/s00261-021-03235-0.