The Probability of Aggressive Versus Indolent Histology Based on Renal Tumor Size: Implications for Surveillance and Treatment

Bhindi, Bimal; Thompson, R. Houston; Lohse, Christine M.; Mason, Ross; Frank, Igor; Costello, Brian A.; Potretzke, Aaron M.; Hartman, Robert P.; Potretzke, Theodora A.; Boorjian, Stephen A.; Cheville, John C.; Leibovich, Bradley C.

doi:10.1016/j.eururo.2018.06.003

Cited by 103 publications

(84 citation statements)

References 39 publications

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“…In a cohort study by Bhindi et al [32], on 2650 patients who underwent partial or radical nephrectomy for pT1-2 pNx/0 M0 RCC, the percentages of malignant/aggressive histology were higher for men. Moreover, the likelihood of malignancy for a given tumor size in men was comparable to tumors twice as large in women.…”

Section: Impact Of Gender On Prognosismentioning

confidence: 95%

Gender-Related Approach to Kidney Cancer Management: Moving Forward

Mancini

Righetto

Baggio

2020

IJMS

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Men are more frequently diagnosed with kidney cancer than women, with a more aggressive histology, larger tumors, a higher grade and stage, and worse oncological outcomes. Smoking habits and sex steroid hormones seem to have a possible role in explaining these gender disparities. Moreover, the expression of genes involved in tumor growth and immune response in kidney cancer varies between men and women, having an impact on the gender-related response to oncological therapy, such as anti-angiogenic drugs and immunotherapy. Recent advances have been made in our understanding of the molecular and genetic mechanisms involved in kidney cancer, which could partially explain the gender differences, and they are summarized in this paper. However, other key mechanisms, which fully clarify the striking clinical gender-related differences observed in kidney cancer, are not completely understood at present. We reviewed and summarized the most relevant publications about the relationship between gender and kidney cancer. Efforts should be made to progress in bench and clinical research on gender-related signatures and disparities, and their impact on the clinical management of kidney cancer.

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Section: Impact Of Gender On Prognosismentioning

confidence: 95%

Gender-Related Approach to Kidney Cancer Management: Moving Forward

Mancini

Righetto

Baggio

2020

IJMS

View full text Add to dashboard Cite

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“…Tumor size in the model, as a primary predictor, suggested that larger tumors are more prone to tumor necrosis, which is consistent with previous studies in which tumor size was reported to be a significant independent factor for invasive The DCA demonstrated that if the threshold probability was >5%, the application of radiomics nomogram to predict tumor necrosis adds more benefit than treating all or none of the patients, radiomics signatrue and image features model. biological behavior of ccRCC (5). In addition, the results show that the imaging findings of intratumoral vessels are more common in ccRCC with tumor necrosis, which may be related to the mechanism of tumor necrosis; more specifically, excessive blood supply, immature blood vessels, and hypoxia associated with vascular remodeling in the tumor (7,22,23).…”

Section: Discussionmentioning

confidence: 86%

“…However, histopathological features, including hemorrhage, cystic transformation, hyalinization, as well as foci of fibrosis, should not necessarily be regarded as tumor necrosis (3). Numerous studies have demonstrated that the presence of tumor necrosis is a reflection of aggressive behavior and an independent predictor of poor survival in patients with ccRCC (5,6). Therefore, The International Society of Urological Pathology (ISUP) recommended that tumor necrotic pathological information should be routinely included in pathological reports for ccRCC (7).…”

Section: Introductionmentioning

confidence: 99%

A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma

Jiang

Huang

et al. 2020

Front. Oncol.

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“…A radiomic signature in the current study was constructed using eleven selected features including shape features, first-order feature, and texture features. Pathologically, tumor size is an important indicator of tumor staging and associated with higher nuclear grade, more histologic necrosis, and sarcomatoid changes ( 15 , 20 – 22 ). As a consequence, the shape features, especially the major axis length, which is the largest axis length of the tumor, contributed to predicting the SSIGN risk groups.…”

Section: Discussionmentioning

confidence: 99%

Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation

Jiang

Huang

et al. 2020

Front. Oncol.

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The stage, size, grade, and necrosis (SSIGN) score can facilitate the assessment of tumor aggressiveness and the personal management for patients with clear cell renal cell carcinoma (ccRCC). However, this score is only available after the postoperative pathological evaluation. The aim of this study was to develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with ccRCC in multicenters. Methods: In total, 330 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (scores 0-3) and intermediateto high-risk group (score ≥ 4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furthermore, the predictive performance of the above models for the SSIGN risk groups was evaluated with regard to their discrimination, calibration, and clinical usefulness. Results: A radiomic signature consisting of sixteen relevant features from the nephrographic phase CT images achieved a good calibration (all Hosmer-Lemeshow p > 0.05) and favorable prediction efficacy in the training cohort [area under the curve (AUC): 0.940, 95% confidence interval (CI): 0.884-0.973] and in the external validation cohorts (AUC: 0.876, 95% CI: 0.811-0.942; AUC: 0.928, 95% CI: 0.844-0.975, respectively). Jiang et al. Radiomics Predicts SSIGN Risk Groups The radiomic signature performed better than the image feature model constructed by intra-tumoral vessels (all p < 0.05) and showed similar performance with the fusion model integrating radiomic signature and intra-tumoral vessels (all p > 0.05) in terms of the discrimination in all cohorts. Moreover, the decision curve analysis verified the clinical utility of the radiomic signature in both external cohorts. Conclusion: Radiomic signature could be used as a promising non-invasive tool to predict SSIGN risk groups and to facilitate preoperative clinical decision-making for patients with ccRCC.

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The Probability of Aggressive Versus Indolent Histology Based on Renal Tumor Size: Implications for Surveillance and Treatment

Cited by 103 publications

References 39 publications

Gender-Related Approach to Kidney Cancer Management: Moving Forward

Gender-Related Approach to Kidney Cancer Management: Moving Forward

A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma

Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation

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