Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I–III Renal Cell Carcinoma

Zhang, Haijie; Yin, Fang‐Fang; Chen, Menglin; Yang, Liyang; Qi, Anqi; Cui, Weiwei; Yang, Shanshan; Wen, Gen

doi:10.3389/fonc.2021.742547

Cited by 10 publications

(12 citation statements)

References 45 publications

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“…Multiple studies have developed diverse radiomics models for the prediction of recurrence and metastasis in clear cell carcinoma [19,[27][28][29]. For example, Bing Kang et al created a radiomic model for use in T1 stage ccRCC consisting of ten intratumoral features for the prediction of postoperative recurrence risks [28], and achieve a high AUC (training, 0.91; validation, 0.92).…”

Section: Discussionmentioning

confidence: 99%

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Multiregional radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

Li,

Guo,

Wang

et al. 2024

Preprint

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Background The selection of individualized treatment options based on the risk of recurrence is crucial in the adjuvant treatment of clear cell renal cell carcinoma(ccRCC). Multiregional radiomics might noninvasively obtain potential information about the intratumoral and peritumoral heterogeneity of ccRCC and reveal the prognostic information behind the images. This study aimed to develop a CT-based multiregional radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized ccRCC. Methods A total of 395 patients with pathologically diagnosed ccRCC were included in the training (n = 281) and internal validation set (n = 114). Multiregion radiomics features from both the intratumoral and peritumoral areas were extracted. The selection of radiomics features and clinicopathological factors was performed using the least absolute shrinkage and selection operator (LASSO) Cox regression. A final model (FM) for the radiomics nomogram was developed, which incorporated the selected clinicopathological and radiomics features predictors based on multivariate Cox proportional hazard regression. The performance of the model was assessed using receiver operator characteristic (ROC) analysis. Results The radiomics nomogram demonstrated excellent prediction performance in both the training and validation sets. The discriminatory ability of the radiomics nomogram was superior to that of the clinical model (C-index 0.926 vs .0.898, P < 0.05). Decision curve analysis revealed that the nomogram had more net benefit than the clinical model. Conclusions The use of a radiomic nomogram with multiregion features improved the stratification of postoperative recurrence risk in patients with localized ccRCC and can be considered a valuable tool in clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

“…However, the study is limited mainly by its relatively small sample size and recruiting only 168 patients. While other studies [19,20,27,29] have included stage I-III patients, they have utilized the predictive capacities of intratumoral radiomics (C-index 0.706-0.780). The accuracy of these models in predicting RFS is limited.…”

Section: Discussionmentioning

confidence: 99%

Multiregional radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

Li,

Guo,

Wang

et al. 2024

Preprint

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“…Finally, 39 studies were accepted and included. Four papers were pertinent to bladder cancer [ 6 – 9 ], and 18 to renal cancer [ 10 , 11 , 20 – 27 , 12 – 19 ]. Figure 2 shows the flow chart of the literature search.…”

Section: Methodsmentioning

confidence: 99%

Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review

2023

Cent european J Urol

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Introduction Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and grading of renal and bladder cancer. Material and methods A literature search was performed in June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. Results Twenty-two papers were included, 4 were pertinent to bladder cancer, and 18 to renal cancer. Radiomics outperforms the visual assessment by radiologists in contrast-enhanced computed tomography (CECT) to predict muscle invasion but are equivalent to CT reporting by radiologists in predicting lymph node metastasis. Magnetic resonance imaging (MRI) radiomics outperforms radiological reporting for lymph node metastasis. Radiomics perform better than radiologists reporting the probability of renal cell carcinoma, improving interreader concordance and performance. Radiomics also helps to determine differences in types of renal pathology and between malignant lesions from their benign counterparts. Radiomics can be helpful to establish a model for differentiating low-grade from high-grade clear cell renal cancer with high accuracy just from contrast-enhanced CT scans. Conclusions Our review shows that radiomic models outperform individual reports by radiologists by their ability to incorporate many more complex radiological features.

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“…The study focused on the reproducibility and accuracy of texture features from MR and CT images, finding that MR-and CT-based models effectively distinguished high-from low-grade ccRCCs. Zhang et al [6] focused on investigating radiomics features (RFs) related to the progression-free survival of RCC, aiming to develop a nomogram for individualized treatment reference. The research involved analyzing RFs and clinical data from 175 patients, using enhanced CT imaging and the LASSO algorithm for feature selection.…”

Section: Introductionmentioning

confidence: 99%

“…In the realm of kidney cancer, particularly renal cell carcinoma (RCC), with its inherent heterogeneity, AI and radiomics adeptly address diagnostic and prognostic challenges. ML algorithms, when applied to MRI-derived radiomics features, have shown promise in distinguishing RCC subtypes and grades [5,6]. Cui et al [5] investigated MR-and CT-based ML models for grading clear cell RCC.…”

Section: Introductionmentioning

confidence: 99%

Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review

Feretzakis,

Juliebø-Jones,

Tsaturyan

et al. 2024

Cancers

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This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the “black box” nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.

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Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I–III Renal Cell Carcinoma

Cited by 10 publications

References 45 publications

Multiregional radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

Multiregional radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review

Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review

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