Purpose To determine the prostate cancer detection rate of multi-parametric (MP) MRI at 3T. Precise one to one histopathologic correlation with MRI was possible using prostate MRI based custom-printed specimen molds following radical prostatectomy. Materials and methods This IRB approved prospective study included forty-five patients (mean age 60.2 years, range 49–75 years) with a mean PSA of 6.37ng/mL (range 2.3–23.7ng/mL), who had biopsy proven prostate cancer (mean Gleason score of 6.7; range 6 to 9). Prior to prostatectomy, all patients underwent prostate MRI on a 3T scanner which included tri-plane T2 weighted MRI, apparent diffusion coefficient maps of diffusion weighted MRI, dynamic contrast enhanced MRI, and spectroscopy.. The prostate specimen was whole mount sectioned in the mold allowing geometric alignment to MRI. Tumors were mapped on MRI and histopathology.. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of MRI for cancer detection were calculated. Additionally, the effects of tumor size and Gleason score on sensitivity of MP-MRI were evaluated. Results PPV of MP-MRI to detect prostate cancer was 98%, 98%, and 100% in overall prostate, peripheral zone, and central gland, respectively. Sensitivities of MRI sequences were higher for tumors >5mm in diameter, as well as for tumors with higher Gleason scores (>7) (p<0.05). Conclusion Prostate MRI at 3T allows for the detection of prostate cancer. A multi-parametric approach increases the predictive power of MRI for diagnosis. In this study, accurate correlation between MP-MRI and histopathology was obtained by the patient specific MRI-based mold technique.
To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18 F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods: Prospective 18 F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results: The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P , .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.
IMPORTANCE Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. OBJECTIVE To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. MAIN OUTCOMES AND MEASUREMENTS Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. RESULTS Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive Յ12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. CONCLUSIONS AND RELEVANCE While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine (continued)
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