Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study

Li, Jiance; Weng, Zhiliang; Xu, Hui; Zhang, Zhao; Miao, Haiwei; Chen, Wei; Liu, Zheng; Zhang, Xiaoqin; Wang, Meihao; Xu, Xinyun; Ye, Qiong

doi:10.1016/j.ejrad.2017.11.001

Cited by 75 publications

(56 citation statements)

References 26 publications

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“…Our study has limitations. The sample size was relatively small, although comparable to what has been published previously regarding the MRI appearance of TZ PCa and BPH and larger than the recent study by Li et al, who performed similar quantitative analysis of TZ abnormalities on prostate MRI but without subgroup analysis of stromal BPH or small lesions. We included TZ PCa diagnosed at RP or targeted biopsy to increase our sample size but included only BPH nodules diagnosed after RP.…”

Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

“…Quantitative ADC and texture features have previously been investigated and found to be significant for diagnosis of cancer using logistic regression and machine‐learning models in both the PZ and TZ . Among features studied, mean ADC, skewness, and entropy were all among the reported significant discriminatory features that could differentiate between clinically significant and low‐risk (Gleason score = 6) cancers, as well as between Gleason score 3 + 4 vs. Gleason score 4 + 3 tumors using SVM and other machine‐learning models . Li et al reported AUC values using an initial set of features compared with the best performing subset/technique varying between 0.97 (CI 0.94–0.99) and 0.91 (CI 0.85–0.95) evaluating strictly TZ abnormalities, while Fehr et al reported AUC values between 0.60 and 0.99 (CIs not provided) evaluating the TZ and PZ together …”

Section: Discussionmentioning

confidence: 99%

“…The details of the SVM modeling, including exploration of nonlinear relationships to provide the widest plane of separation between the two outcomes and crossvalidation, performed in our study have been described elsewhere. 36 Comparisons of overall accuracy between different variables for diagnosis of BPH vs. TZ PCa were performed using the method described by DeLong.…”

Section: Discussionmentioning

confidence: 99%

“…The area under curve (AUC) was reported with sensitivity and specificity for diagnosis of TZ PCa (using the method of Youden to determine the criterion that corresponded to the maximum accuracy for parametric data). The details of the SVM modeling, including exploration of nonlinear relationships to provide the widest plane of separation between the two outcomes and crossvalidation, performed in our study have been described elsewhere . Comparisons of overall accuracy between different variables for diagnosis of BPH vs. TZ PCa were performed using the method described by DeLong.…”

Section: Methodsmentioning

confidence: 99%

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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

Krishna

Thornhill

et al. 2019

Magnetic Resonance Imaging

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Background The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI‐RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions. Purpose To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa. Study Type Retrospective. Population Ninety patients; 44 consecutive TZ PCa were compared with 61 consecutive BPH nodules (26 glandular/35 stromal). Field Strength/Sequence 3 T/T2‐weighted (T2W) fast spin‐echo, diffusion weighted imaging. Assessment A radiologist manually segmented lesions on axial images for quantitative ADC (mean, 10th, 25th‐centile‐ADC), T2W‐shape (circularity, convexity) and T2W‐texture (kurtosis, skewness, entropy, run‐length nonuniformity [RLNU], gray‐level nonuniformity [GLNU]) analysis. A second radiologist segmented one‐fifth of randomly selected lesions to determine the reproducibility of measurements. The reference standard was histopathology for all lesions. Statistical Tests Quantitative features were selected a priori and were compared using univariate and multivariate analysis. LR and SVM models of statistically significant features were constructed and evaluated using receiver operator characteristic (ROC) analysis. Subgroup analysis of TZ PCa vs. only stromal BPH and in lesions measuring <15 mm was performed. Agreement in measurements was assessed using the Dice similarity coefficient (DSC). Results Mean, 25th and 10th‐centile ADC, circularity, and texture (entropy, RLNU, GLNU) features differed between groups (P < 0.0001–0.0058); however, at multivariate analysis only circularity and ADC metrics (P < 0.001) remained significant. LR and SVM models were highly accurate for the diagnosis of TZ PCa (sensitivity/specificity/AUC): 93.2%/98.4%/0.989 and 93.2%/96.7%/0.949, respectively, with no significance difference between the LR and SVM models (P = 0.2271). Reproducibility of segmentation was excellent (DSC 0.84 tumors and 0.87 BPH). Subgroup analyses of TZ PCa vs. stromal BPH (AUC = 0.976) and in <15 mm lesions (AUC = 0.990) remained highly accurate. Data Conclusion LR and SVM models incorporating previously described quantitative ADC, shape and texture analysis features are highly accurate for the diagnosis of TZ PCa and remained accurate when comparing TZ PCa with stromal BPH and in smaller lesions. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:940–950.

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Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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

Krishna

Thornhill

et al. 2019

Magnetic Resonance Imaging

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Assessment of prostate cancer prognostic Gleason grade group using zonal‐specific features extracted from biparametric MRI using a KNN classifier

Jensen

Carl

Boesen

et al. 2019

J Applied Clin Med Phys

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Purpose: To automatically assess the aggressiveness of prostate cancer (PCa) lesions using zonal-specific image features extracted from diffusion weighted imaging (DWI) and T2W MRI. Methods: Region of interest was extracted from DWI (peripheral zone) and T2W MRI (transitional zone and anterior fibromuscular stroma) around the center of 112 PCa lesions from 99 patients. Image histogram and texture features, 38 in total, were used together with a k-nearest neighbor classifier to classify lesions into their respective prognostic Grade Group (GG) (proposed by the International Society of Urological Pathology 2014 consensus conference). A semi-exhaustive feature search was performed (1-6 features in each feature set) and validated using threefold stratified cross validation in a one-versus-rest classification setup. Results: Classifying PCa lesions into GGs resulted in AUC of 0.87, 0.88, 0.96, 0.98, and 0.91 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5 for the peripheral zone, respectively. The results for transitional zone and anterior fibromuscular stroma were AUC of 0.85, 0.89, 0.83, 0.94, and 0.86 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5, respectively. Conclusion: This study showed promising results with reasonable AUC values for classification of all GG indicating that zonal-specific imaging features from DWI and T2W MRI can be used to differentiate between PCa lesions of various aggressiveness.

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From cancer big data to treatment: Artificial intelligence in cancer research

Danishuddin,

Khan,

Kim

2023

The Journal of Gene Medicine

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In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high‐dimensional multi‐omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

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Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study

Cited by 75 publications

References 26 publications

Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Assessment of prostate cancer prognostic Gleason grade group using zonal‐specific features extracted from biparametric MRI using a KNN classifier

From cancer big data to treatment: Artificial intelligence in cancer research

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