Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values

Abstract

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis. Global and zone-specific random forest RML and mean ADC models for classification of clinically significant prostate cancer (Gleason grade group ≥ 2) were developed on the training set and the fixed models tested on an independent test set. Clinical readings, mean ADC, and radiomics were compared by using the McNemar test and receiver operating characteristic (ROC) analysis. Results In the test set, radiologist interpretation had a per-lesion sensitivity of 88% (53 of 60) and specificity of 50% (79 of 159). Quantitative measurement of the mean ADC (cut-off 732 mm/sec) significantly reduced false-positive (FP) lesions from 80 to 60 (specificity 62% [99 of 159]) and false-negative (FN) lesions from seven to six (sensitivity 90% [54 of 60]) (P = .048). Radiologist interpretation had a per-patient sensitivity of 89% (40 of 45) and specificity of 43% (38 of 88). Quantitative measurement of the mean ADC reduced the number of patients with FP lesions from 50 to 43 (specificity 51% [45 of 88]) and the number of patients with FN lesions from five to three (sensitivity 93% [42 of 45]) (P = .496). Comparison of the area under the ROC curve (AUC) for the mean ADC (AUC = 0.84; AUC ≤ 0.87) vs the RML (AUC = 0.88, P = .176; AUC ≤ 0.89, P ≥ .493) showed no significantly different performance. Conclusion Quantitative measurement of the mean apparent diffusion coefficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment. Radiomic machine learning had comparable but not better performance than mean ADC assessment. © RSNA, 2018 Online supplemental material is available for this article.

Machine and deep learning methods for radiomics

“…Breast cancer lesions, automatically detected using connected component labeling and adaptive fuzzy region growing algorithm, were classified using radiomic features as benign mass or malignant tumor on digital mammography, dynamic contrast‐enhanced (DCE) MRI, and ultrasound . A radiomic model based on mean apparent diffusion coefficient (ADC), had better accuracy than radiologist assessment for characterization of prostate lesions as clinically significant cancer (Gleason grade group ≥ 2) during prospective MRI interpretation . A deep learning multiparametric MRI transfer learning method has also shown the ability to classify prostate cancer high grade/low or grade .…”

Interprofessionelle Kooperation in klinischen Studien

“…Und auch in der Bildgebungsforschung kommt es mehr und mehr zu interprofessioneller Zusammenarbeit, etwa zwischen Mediziner und (Medizin-)Physiker und -Informatiker bei Digitalisierungsfragen im Bereich künstlicher Intelligenz, maschinellem Lernen usw. [28,29]. Aus diesem Grund wird nach Ein-schätzung der Autoren die Bildgebung insgesamt künftig eine noch stärkere Vorreiterrolle bei der interprofessionellen Kooperation und Kommunikation in klinischen Forschungsprojekten und -studien einnehmen.…”

“…Angesichts des täglichen Termindrucks hat es sich darüber hinaus bewährt, Ressourcen für einen studienerfahrenen Mitarbeiter einzuplanen, der als Fachrichtungs-übergreifender Koordinator/Projektmanager studienrelevante Als Folge der zunehmenden Forschungsvernetzung werden bei zukünftigen oftmals multizentrischen klinischen Studi-en natürlicherweise Experten verschiedener Professionen zusammenarbeiten. Schon heute können viele Forschungsprojekte nur dann erfolgreich in die klinische Anwendung und Entwicklung transferiert werden, wenn Mediziner mit Experten aus den Lebenswissenschaften [33,34], der Informatik [28,29], Ökonomie [35] usw. berufsübergreifend kooperieren und kommunizieren.…”

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

“…Two datasets were used in this study: first, the ChestX-ray dataset released by the NIH in 2017, containing 112,120 frontal radiographs of 30,805 unique patients [10]. At the time of its publication this dataset comprised 8 disease entities and was later updated to contain 14 pathologies [26].…”

“…The application of Artificial Intelligence (AI) in medicine promises to personalize diagnosis, decision management and therapy based on the combination of patient information with knowledge of thousands of experts and the outcome of billions of patient [1][2][3][4]. In recent years, a lot of scientific effort has focused on applications of AI in medicine with a particularly strong focus on radiology [5][6][7][8][9][10]. Whenever there has been progress towards this vision of an omniscient radiological AI, it has mostly been anticipated by corresponding technical advances in the field of Computer Vision on natural images.…”