The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

Tang, Xin; Pang, Tong; Yan, Wei‐Feng; Qian, Wen-Lei; Gong, Youling; Yang, Zhigang

doi:10.3389/fonc.2021.591502

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…In particular, our radiomic signature can better identify both benign and malignant lesions succeeding in the aim of decreasing the overtreatment and of better delineating a malignancy risk stratification and subsequent approach for malignant SRMs. Moreover, these data can be implemented with clinical, deep learning, radiometabolomics, SPECT and transcriptomics data [ 29 , 30 , 31 , 32 , 33 ] to improve performances. Klontzas et al [ 32 ] showed that the radiomics-only performance for distinguishing benign from malignant renal masses was 70%, while the integration of radiomics and metabolomics increased the performance in differentiating malignant lesions (solid, cystic or mixed) to at least 86%.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, Klontzas et al [ 30 ], by combining the 99m Tc Sestamibi uptake with radiomics in distinguishing benign oncocytic neoplasia, increased the diagnostic accuracy and improved positive and negative predictive value. Finally, transcriptomics and radiomics have been combined to assess the prognosis of RCC patients, as mentioned by Tang et al [ 29 ] (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively).…”

Section: Discussionmentioning

confidence: 99%

“…In particular, o radiomic signature can better identify both benign and malignant lesions succeeding the aim of decreasing the overtreatment and of better delineating a malignancy r stratification and subsequent approach for malignant SRMs. Moreover, these data can implemented with clinical, deep learning, radiometabolomics, SPECT a transcriptomics data [29][30][31][32][33] to improve performances. Klontzas et al [32] showed that t The tumor histotypes of the 17 patients belonging to the test set and the ones that are mislabelled are reported in Supplementary Materials (Table S1).…”

Section: Discussionmentioning

confidence: 99%

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Small Renal Masses: Developing a Robust Radiomic Signature

Maddalo,

Bertolotti,

Mazzilli

et al. 2023

Cancers

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(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38–87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Small Renal Masses: Developing a Robust Radiomic Signature

Maddalo,

Bertolotti,

Mazzilli

et al. 2023

Cancers

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“…Many studies have demonstrated that radiomics can predict the prognosis of malignant tumors such as gastric cancer, [15] non-small-cell lung cancer, [16,17] and clear cell renal cell carcinoma. [18,19] This study aimed to establish a combined model based on image features extracted from preoperative MR images to predict recurrence-free survival (RFS) in patients with TNBC.…”

Section: The Authors Have No Funding and Conflicts Of Interest To Dis...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy

Zhao,

Zhang,

Liu

et al. 2023

Medicine

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To explore the value of a radiomics signature and develop a nomogram combined with a radiomics signature and clinical factors for predicting recurrence-free survival in triple-negative breast cancer patients. We enrolled 151 patients from the cancer imaging archive who underwent preoperative contrast-enhanced magnetic resonance imaging. They were assigned to training, validation and external validation cohorts. Image features with coefficients not equal to zero in the 10-fold cross-validation were selected to generate a radiomics signature. Based on the optimal cutoff value of the radiomics signature determined by maximally selected log-rank statistics, patients were stratified into high- and low-risk groups in the training and validation cohorts. Kaplan–Meier survival analysis was performed for both groups. Kaplan–Meier survival distributions in these groups were compared using log-rank tests. Univariate and multivariate Cox regression analyses were used to construct clinical and combined models. Concordance index was used to assess the predictive performance of the 3 models. Calibration of the combined model was assessed using calibration curves. Four image features were selected to generate the radiomics signature. The Kaplan–Meier survival distributions of patients in the 2 groups were significantly different in the training (P < .001) and validation cohorts (P = .001). The C-indices of the radiomics model, clinical model, and combined model in the training and validation cohorts were 0.772, 0.700, 0.878, and 0.744, 0.574, 0.777, respectively. The C-indices of the radiomics model, clinical model, and combined model in the external validation cohort were 0.778, 0.733, 0.822, respectively. The calibration curves of the combined model showed good calibration. The radiomics signature can predict recurrence-free survival of patients with triple-negative breast cancer and improve the predictive performance of the clinical model.

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Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization

Gharibi,

Hajianfar,

Sabouri

et al. 2024

Medical Physics

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BackgroundCoronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single‐photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non‐invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions.PurposeWe aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI‐SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions.Materials and methodsA total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI‐SPECT images were included. Images were reconstructed using 15 combinations of filter cut‐off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal–Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t‐test, Wilcoxon rank‐sum, and signed‐rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features.ResultsBefore applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC < 0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first‐order (FO) and gray‐level co‐occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray‐tone difference matrix (NGTDM), GLCM, gray‐level run length matrix (GLRLM), gray‐level size zone matrix (GLSZM), and gray‐level dependence matrix (GLDM) under Cut‐off, Filter, and Order batches. The Wilcoxon rank‐sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups.ConclusionThe majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI‐SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility.

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The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

Cited by 6 publications

References 29 publications

Small Renal Masses: Developing a Robust Radiomic Signature

Small Renal Masses: Developing a Robust Radiomic Signature

MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization

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