Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Brancato, Valentina; Aiello, Marco; Basso, Luca; Monti, Serena; Palumbo, L.; Costanzo, Giuseppe; Salvatore, Marco; Ragozzino, Alfonso; Cavaliere, Carlo

doi:10.1038/s41598-020-80749-5

Cited by 32 publications

(17 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PCa is high cellular tissue, which restricts the diffusion to some extent due to the blocking of the random movement of water molecules in the tumor. The degree of diffusion limitation is positively correlated with the tumor grade, invasiveness and stage [35]. ML can quantify subtle changes in the diffusion motion of water molecules in the DWI/ADC diagram, which makes DWI perform better than other sequences to evaluate the PCa.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Radiomics Model to Predict Benign and Malignant PI-RADS v2.1 Category 3 lesions : A Retrospective Multi-center Study

Jin

Shen

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

Purpose: To develop machine learning-based prediction models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. Methods: The pre-biopsy MRI datas of 463 patients diagnosed as PI-RADS 3 lesions were collected from 4 medical institutions. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC maps. The ANOVA feature ranking method and support vector machine (SVM) classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The area under the receiver operating characteristic curve (AUC) was used to quantify the predictive performance of each model. Hosmer-lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. Results: T2WI-model with the mean AUC of 0.717 for predicting clinically significant prostate cancer (csPCa) (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.624 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.763 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). Conclusions: The radiomics model based on mechine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions and the generalization ability between different date set.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Radiomics Model to Predict Benign and Malignant PI-RADS v2.1 Category 3 lesions : A Retrospective Multi-center Study

Jin

Shen

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The extraction of radiomic features from 3D Regions of Interest (ROIs) on DCE-MRI subtraction series with the highest mean signal intensity within the ROI [ 26 , 27 , 28 ], PC DCE-MRI, registered ADC, and resliced T2 images was performed using the open source PyRadiomics package [ 29 ]. The obtained features can be classified into five classes: (i) shape features (n = 14); (ii) first-order features (n = 18); (iii) 73 s-order textural statistics including grey-level co-occurrence matrix (GLCM) (n = 24), grey-level run length matrix (GLRLM) (n = 16), grey-level size zone matrix (GLSZM) (n = 16), neighboring grey tone difference matrix (NGTDM) (n = 5), and grey-level dependence matrix (GLDM) (n = 14); 1092 transformed first-order and textural features including (iv) 728 wavelet features in frequency channels LHL, LLH, HHH, HLH, HLL, HHL, LHH, and LLL, where L and H are low- and high-pass filters, respectively; and (v) 364 Laplacian of Gaussian filtered features with sigma ranging from 2.0 to 5.0, with a step size = 1.…”

Section: Materials and Methodsmentioning

confidence: 99%

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Brancato

Esposito

Rosa

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

show abstract

“…Logistic regression modeling yielded AUC of 0.872 in the training cohort and 0.823 in the test cohort. Brancato et al , 131 aimed to investigate the potential use of radiomics for detection of PCa with GS ⩾ 6 in PIRADS 3 images and in peripheral PIRADS 3 upgraded to PIRADS 4 images. RFs were extracted from T2w, ADC map, and DCE-MRI images using specific software.…”

Section: Radiomicsmentioning

confidence: 99%

Radiomics in prostate cancer: an up-to-date review

Ferro

Cobelli

Musi

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

show abstract

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Cited by 32 publications

References 63 publications

Machine Learning-Based Radiomics Model to Predict Benign and Malignant PI-RADS v2.1 Category 3 lesions : A Retrospective Multi-center Study

Machine Learning-Based Radiomics Model to Predict Benign and Malignant PI-RADS v2.1 Category 3 lesions : A Retrospective Multi-center Study

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Radiomics in prostate cancer: an up-to-date review

Contact Info

Product

Resources

About