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Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis
Research article (Journal of Magnetic Resonance Imaging, 2019) · cited 53× · AI/ML
Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis
Summary
Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis is a scholarly article[1].
Key Facts
Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis. Retrieved May 24, 2026, from https://4ort.xyz/entity/transition-zone-prostate-cancer-logistic-regression-and-machinelearning-models-of-quantitative-adc-shape-and-texture-fea
MLA“Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/transition-zone-prostate-cancer-logistic-regression-and-machinelearning-models-of-quantitative-adc-shape-and-texture-fea.
BibTeX@misc{4ortxyz_transition-zone-prostate-cancer-logistic-regression-and-machinelearning-models-of-quantitative-adc-shape-and-texture-fea_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis}}, year = {2026}, url = {https://4ort.xyz/entity/transition-zone-prostate-cancer-logistic-regression-and-machinelearning-models-of-quantitative-adc-shape-and-texture-fea}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis — https://4ort.xyz/entity/transition-zone-prostate-cancer-logistic-regression-and-machinelearning-models-of-quantitative-adc-shape-and-texture-fea (retrieved 2026-05-24)
