Machine Learning in Radiology: Applications Beyond Image Interpretation

Lakhani, Paras; Prater, Adam; Hutson, R. Kent; Andriole, Kathy P.; Dreyer, Keith J.; Morey, Jose; Prevedello, Luciano M.; Clark, Toshi J.; Geis, J. Raymond; Itri, Jason N.; Hawkins, C. Matthew

doi:10.1016/j.jacr.2017.09.044

Cited by 210 publications

(131 citation statements)

References 56 publications

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“…Moreover, the introduction of such algorithms in clinical practice requires integration with preexisting workflows, along with an actual demonstration of their value in terms of cost reduction and outcome improvement. The possible applications of machine learning to assist the radiologist during routine clinical activity range from the automatic creation of study protocols to the hanging of study protocols, and to the improvement of computed tomography (CT) image quality; among the various advantages is also a reduction in the radiation dose . In addition, many recent articles have highlighted the ability to use deep convolutional neural networks (DCNNs) to assist radiologist interpretation of radiographic images …”

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confidence: 99%

“…The possible applications of machine learning to assist the radiologist during routine clinical activity range from the automatic creation of study protocols 5 to the hanging of study protocols, and to the improvement of computed tomography (CT) image quality; among the various advantages is also a reduction in the radiation dose. 6,7 In addition, many recent articles have highlighted the ability to use deep convolutional neural networks (DCNNs) to assist radiologist interpretation of radiographic images. 8,9 Deep learning is a branch of machine learning that uses patterns gained directly from data, through a general-purpose learning procedure instead of human-engineered features, to make predictions.…”

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confidence: 99%

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Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Banzato

Causin

Puppa

et al. 2019

Magnetic Resonance Imaging

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Background Grading of meningiomas is important in the choice of the most effective treatment for each patient. Purpose To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. Study Type Retrospective. Population In all, 117 meningioma‐affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. Field Strength/Sequence 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2). Assessment WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception‐V3 and AlexNet DCNNs was tested on ADC maps and PCT1W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance. Statistical Test Leave‐one‐out cross‐validation. Results The application of the Inception‐V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88–0.98). Remarkably, only 1/38 WHO Grade II–III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59–0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II–III cases. The discriminating accuracy of both DCNNs on postcontrast T1W images was low, with Inception‐V3 displaying an AUC of 0.68 (95% CI, 0.59–0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45–0.64). Data Conclusion DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1W images. Level of evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152–1159.

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Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Banzato

Causin

Puppa

et al. 2019

Magnetic Resonance Imaging

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“…In recent years, deep-learning (DL) has become the method of choice for automated image analysis (Lakhani et al, 2018). Radiology studies have reported on application of deep learning in different organs (Lehman et al, 2018;Nam et al, 2018;Tao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

Polymeri

Sadik²,

Kaboteh³

et al. 2019

Clin Physio Funct Imaging

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Aim To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/ CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-na€ ıve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18 F-choline PET images above a standardized uptake value (SUV) of 2Á65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results The SDI between the automated and the manual volume segmentations was 0Á78 and 0Á79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0Á02), whereas age, PSA, and Gleason score were not. Conclusion Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

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“…Advanced data science methods such as machine learning offer a promising alternative to detect latent variables that underlie complex clinical phenotypes such as HIV [14][15][16][17][18][19][20][21]. In studies of structural neuroimaging, Wade et al [19] achieved 72% accuracy classifying adults as either HIV-infected or HIV-uninfected, and Zhang et al [20] achieved 85% accuracy discriminating older adults with HIV from age-matched adults with early Alzheimer's disease.…”

Section: Introductionmentioning

confidence: 99%

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Paul

Cho

Mellins

et al. 2019

Preprint

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Background: A subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-tomiddle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV. Methods:Analyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity. Results: The analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors. Conclusion: Machine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.

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Machine Learning in Radiology: Applications Beyond Image Interpretation

Cited by 210 publications

References 56 publications

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

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