IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were ...both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, ...observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on ...subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (
= 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.
•Rats will press a lever to procure a CS that had been previously paired with food.•The number of CS-food pairings determines whether the reinforcing properties of the CS can be disrupted by ...extinction.•Where extinction fails to disrupt these properties, they can be disrupted by shifting rats from CS-food pairings to explicitly unpaired presentations of the CS and food.•This effect of the unpaired treatment (disruption) differs from its effect in studies of Pavlovian-to-instrumental transfer (no disruption), suggesting that it disrupts the reinforcing properties of the CS (assessed here) while sparing its other properties (assessed in PIT).•These findings are consistent with the Rescorla-Wagner (1972) model, according to which the unpaired treatment is more effective than extinction in reducing the strength of the CS-US association.
This series of experiments examined the effects of extinction and an explicitly unpaired treatment on the ability of a conditioned stimulus (CS) to function as a reinforcer. Rats were trained to lever press for food, exposed to pairings of a noise CS and food, and, finally, tested for their willingness to lever press for the CS in the absence of the food. Experiment 1 provided a demonstration of conditioned reinforcement (using controls that were only exposed to unpaired presentations of the CS and food) and showed that it was equivalent after one or four sessions of CS-food pairings. Experiments 2 and 3 showed that, after one session of CS-food pairings, repeated presentations of the CS alone reduced its reinforcing properties; but after four sessions of CS-food pairings, repeated presentations of the CS alone had no effect on these properties. Experiment 4 showed that, after four sessions of CS-food pairings, explicitly unpaired presentations of the CS and food completely undermined conditioned reinforcement. Finally, Experiment 5 provided within-experiment evidence that, after four sessions of CS-food pairings, the reinforcing properties of the CS were disrupted by explicitly unpaired presentations of the CS and food but spared by repeated presentations of the CS alone. Together, these findings indicate that the effectiveness of extinction in undermining the reinforcing properties of a CS depends on its level of conditioning; and that, where extinction fails to disrupt these properties, they are successfully undermined by an explicitly unpaired treatment. They are discussed with respect to findings in the literature on Pavlovian-to-instrumental transfer; and the Rescorla-Wagner model, which anticipates that an explicitly unpaired treatment will be more effective than extinction in reversing the effects of conditioning.
For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, ...resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.
To evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.
The DLS was evaluated using 752 deidentified digitized images of formalin-fixed paraffin-embedded prostate needle core biopsy specimens obtained from 3 institutions in the United States, including 1 institution not used for DLS development. To obtain the Gleason grade group (GG), each specimen was first reviewed by 2 expert urologic subspecialists from a multi-institutional panel of 6 individuals (years of experience: mean, 25 years; range, 18-34 years). A third subspecialist reviewed discordant cases to arrive at a majority opinion. To reduce diagnostic uncertainty, all subspecialists had access to an immunohistochemical-stained section and 3 histologic sections for every biopsied specimen. Their review was conducted from December 2018 to June 2019.
The frequency of the exact agreement of the DLS with the majority opinion of the subspecialists in categorizing each tumor-containing specimen as 1 of 5 categories: nontumor, GG1, GG2, GG3, or GG4-5. For comparison, the rate of agreement of 19 general pathologists' opinions with the subspecialists' majority opinions was also evaluated.
For grading tumor-containing biopsy specimens in the validation set (n = 498), the rate of agreement with subspecialists was significantly higher for the DLS (71.7%; 95% CI, 67.9%-75.3%) than for general pathologists (58.0%; 95% CI, 54.5%-61.4%) (P < .001). In subanalyses of biopsy specimens from an external validation set (n = 322), the Gleason grading performance of the DLS remained similar. For distinguishing nontumor from tumor-containing biopsy specimens (n = 752), the rate of agreement with subspecialists was 94.3% (95% CI, 92.4%-95.9%) for the DLS and similar at 94.7% (95% CI, 92.8%-96.3%) for general pathologists (P = .58).
In this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting ...disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95% CI: 0.66-0.73) and 0.69 (95% CI: 0.64-0.72), and added significant predictive value to a set of nine clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R
= 18% in both validation sets). Next, we generated human-interpretable histologic features by clustering embeddings from a deep-learning-based image-similarity model and showed that they explained the majority of the variance (R
of 73-80%). Furthermore, the clustering-derived feature most strongly associated with high DLS scores was also highly prognostic in isolation. With a distinct visual appearance (poorly differentiated tumor cell clusters adjacent to adipose tissue), this feature was identified by annotators with 87.0-95.5% accuracy. Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.
Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict ...glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10−8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.
Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in ...screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging.
Activating mutations in the neuroblastoma rat sarcoma viral oncogene homolog (NRAS) gene are common genetic events in malignant melanoma being found in 15–25% of cases. NRAS is thought to activate ...both mitogen activated protein kinase (MAPK) and PI3K signaling in melanoma cells. We studied the influence of different components on the MAP/extracellular signal-regulated (ERK) kinase (MEK) and PI3K/mammalian target of rapamycin (mTOR)-signaling cascade in NRAS mutant melanoma cells. In general, these cells were more sensitive to MEK inhibition compared with inhibition in the PI3K/mTOR cascade. Combined targeting of MEK and PI3K was superior to MEK and mTOR ₁,₂ inhibition in all NRAS mutant melanoma cell lines tested, suggesting that PI3K signaling is more important for cell survival in NRAS mutant melanoma when MEK is inhibited. However, targeting of PI3K/mTOR ₁,₂ in combination with MEK inhibitors is necessary to effectively abolish growth of NRAS mutant melanoma cells in vitro and regress xenografted NRAS mutant melanoma. Furthermore, we showed that MEK and PI3K/mTOR ₁,₂ inhibition is synergistic. Expression analysis confirms that combined MEK and PI3K/mTOR ₁,₂ inhibition predominantly influences genes in the rat sarcoma (RAS) pathway and growth factor receptor pathways, which signal through MEK/ERK and PI3K/mTOR, respectively. Our results suggest that combined targeting of the MEK/ERK and PI3K/mTOR pathways has antitumor activity and might serve as a therapeutic option in the treatment of NRAS mutant melanoma, for which there are currently no effective therapies.
Abstract In this study we investigate the effects of nanotubular titanium oxide (TiO2 ) surfaces on vascular cells. EC and VSMC response to nanotubes was investigated through immunofluorescence ...staining, scanning electron microscopy, 5- ethynyl -2′-deoxyuridine proliferation assays, and prostaglandin I2 (PGI2 ) enzyme immunoassays. We found that the nanotubular surface significantly enhances EC proliferation and secretion of PGI2 . The surface also results in a decrease in VSMC proliferation and increased expression of smooth muscle α-actin. These data suggest that engineered nanotopographical cues may influence both EC and VSMC behavior in a manner that may be useful for stent or other vascular applications.
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