Prognostic analysis of postoperative clinically nonmetastatic renal cell carcinoma

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience

BACKGROUND

The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI).

PATIENTS AND METHODS

We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded.

RESULTS

There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively).

CONCLUSIONS

Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.

A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study

BACKGROUND AND PURPOSE

The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC.

METHODS

A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell's concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models.

RESULTS

The DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients.

CONCLUSION

The DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.

Prospective evaluation of hematological parameters in preoperative renal cell cancer patients

BACKGROUND

Of all the genitourinary cancers, renal cell carcinoma (RCC) is still the most common malignancy with high mortality rates. There are still insufficient biomarkers to predict disease prognosis. Systemic inflammation markers play an important role in tumor development and growth. There are studies which show the relationship of fibrinogen and albumin individually with cancer prognosis in many cancers. Fibrinogen/albumin ratio(FAR), on the other hand, has prognostic importance like other inflammation indicators in cancer. Therefore, we investigated whether FAR had a potential value in evaluating the prognosis of patients with nonmetastatic kidney cancer or not.

METHODS

A total of 72 patients who had nephrectomy operation at 19 Mayıs University, Faculty of Medicine between January 2019 and January 2021 and who did not have distant metastasis were included in the study. FAR was calculated from the blood taken from the patients before the nephrectomy operation. The cut-off value was found for this FAR by receiver operating characteristic(ROC) curve analysis. The patients were divided into 2 groups as high- and low-FAR according to this cut-off value. Kaplan Meier test was used to evaluate the predictive value of clinicopathological parameters for overall survival (OS). The Log-rank test was used to determine whether there was a relationship between the preoperative FAR and the clinico-pathological data of the patients.

RESULTS

The best cutoff value for the FAR was 0.114. A FAR > 0.114 was associated with higher Fuhrman Grade (FG) (P < 0.0001) and later pathological T stage (P < 0.0001). Patients with a high FAR (> 0.114) had worse OS [Std. Error 2.932, 95% confidence interval (CI): 73.659-85.154, P < 0.0001]. In addition, a positive significant correlation was found between high grade and platelet lymphocyte ratio (p < 0,020). Furthermore, a significant correlation was found between the pathology t stage of the patients and the platelet lymphocyte ratio (p: 0.020).

CONCLUSIONS

The preoperative FAR is an independent prognostic factor of OS in renal cancer patients. A FAR > 0.114 was significantly related to decreased survival in renal cancer patients. In addition, the platelet-lymphocyte ratio seems to be related to OS, as well as FAR. Further studies are required on this subject.

Novel Blood Indicators of Progression and Prognosis in Renal Cell Carcinoma: Red Cell Distribution Width-to-Lymphocyte Ratio and Albumin-to-Fibrinogen Ratio

Objective

To evaluate the value of preoperative red cell distribution width-to-lymphocyte ratio (RLR) and albumin-to-fibrinogen ratio (AFR) to the prognosis of patients after renal cell carcinoma (RCC).

Methods

From 2012 to 2016, a total of 273 RCC patients underwent radical nephrectomy or partial nephrectomy. This study retrospectively analyzed this group of patients. X-tile software was used to determine the optimal values of RLR and AFR in the peripheral blood. The nomogram constructed with independent factors was used to predict the survival outcome of the patients after RCC.

Results

The RLR of the RCC group was higher than that of the normal control group (P=0.002), whereas the AFR of the RCC group was lower than that of the normal control group (P < 0.001). RLR and AFR are related to tumour type and tumour-node-metastasis (TNM) stage (P < 0.05 for all). Cox regression analysis showed that the independent prognostic factors affecting overall survival and disease-free survival in the RCC group were symptom, tumour type, TNM stage, Fuhrman grade, RLR, and AFR (P < 0.05 for all). The nomogram constructed by multiple factors has better predictive power for patients after RCC.

Conclusion

Preoperative RLR and AFR can serve as potential biomarkers to predict the prognosis of postoperative RCC patients and improve the predictability of patient recurrence and survival.

An immune scores-based nomogram for predicting overall survival in patients with clear cell renal cell carcinoma

The role of immune cell infiltration in the prognosis of clear cell renal cell carcinoma (ccRCC) has received increasing attention. However, immune scores have not yet been introduced into routine clinical practice of ccRCC patients. The principal objective of our research was to study the correlation between immune scores and overall survival (OS) of ccRCC.In this study, Cox regression analyses were used to identify risk factors associated with OS of ccRCC based on the Cancer Genome Atlas datasets. Furthermore, an integrated nomogram combining immune scores and clinicopathologic factors was built for predicting 3- and 5-year OS of ccRCC patients. The receiver operating characteristic curve, concordance index, and calibration curves were used for the evaluation of our nomogram. Also, Kaplan-Meier (KM) survival analysis of immune scores, stromal scores, and different clinicopathological factors was performed.A total of 514 patients were divided into the low- or high-immune scores group. KM and multivariate Cox regression analyses demonstrated that ccRCC patients with high-immune scores had significantly poor OS compared with those with low-immune scores. Calibration curves showed good consistency between the predicted OS and the actual OS probability. Areas under the receiver operating characteristic curves for 3- and 5-year OS were 0.816 and 0.769, and the concordance index was 0.775, indicating that our nomogram had good accuracy for predicting OS of ccRCC patients. Additionally, KM analysis showed that older age, later T stage, distant metastasis, advanced tumor lymph node metastasis stage, higher tumor grade, left site, and low stromal scores were associated with worse OS in ccRCC patients.High-immune scores show a significant correlation with unsatisfactory prognosis in ccRCC patients. Furthermore, the immune scores-based nomogram may be helpful in predicting ccRCC prognosis.

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

Objective: 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 intermediate- to 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). 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.

Isolated Pancreatic Metastases of Renal Cell Carcinoma-A Paradigm of a Seed and Soil Mechanism: A Literature Analysis of 1,034 Observations

Previously documented arguments, in favor of the suspected impact of a seed and soil mechanism, in the development and progression of isolated pancreatic metastasis of renal cell carcinomas (isPM) are: (1) uniform and independent from the side of the primary tumor distribution of isPM within the pancreas and, (2) the similar survival rates for singular and multiple isPM. In addition, the present study adds new arguments that further confirm the importance of an seed and soil mechanism in isPM: (1) Within the singular isPM, the size of the metastasis does not affect the overall survival; (2) Within the group of multiple isPMs, the overall survival does not depend on the number of metastases; (3) For synchronous and metachronous isPM, survival rates are also not different, and (4) Within the group of metachronous isPM there is also no correlation between the overall survival and interval until metastases occurs. This unusual ineffectiveness of otherwise known risk factors of solid cancers can be explained plausibly by the hypothesis of a very selective seed and soil mechanism in isPM. It only allows embolized renal carcinoma cells in the pancreas to complete all steps required to grow into clinically manifest metastases. In all other organs, on the other hand, the body is able to eliminate the embolized tumor cells or at least put them into a dormant state for many years. This minimizes the risk of occult micrometastases in distant organs, which could later—after isPM treatment—grow into clinically manifest metastases, so that the prognosis of the isPM is only determined by an adequate therapy of the pancreatic foci, and prognostic factors, such as total tumor burden or interval until the occurrence of the isPM remain ineffective.

Prognostic analysis of postoperative clinically nonmetastatic renal cell carcinoma

Objectives

To investigate the survival characteristics of postoperative nonmetastatic renal cell carcinoma (RCC) patients, and the predictive value of a prognostic model.

Materials and Methods

We retrospectively evaluated data from 1202 postoperative nonmetastatic RCC patients who were treated between 1999 and 2012 at West China Hospital, Sichuan University (Chengdu, China). In addition, we also evaluated data relating to 53 205 cases acquired from the Surveillance, Epidemiology, and End Results (SEER) program. Survival analysis was performed on the cases, and subgroups, using the Kaplan‐Meier and Cox regression methods. The concordance index of the Stage Size Grade Necrosis (SSIGN), Leibovich, and the UCLA integrated staging system, scores was determined to evaluate the accuracy of these outcome prediction models.

Results

The 5‐year overall survival rate for RCC cases in West China Hospital was 87.6%; this was higher than that observed for SEER cases. Survival analysis identified several factors that exerted significant influence over prognosis, including the time of surgery, Eastern Cooperative Oncology Group performance status, tumor stage, size, nuclear differentiation, pathological subtypes, along with necrotic and sarcomatoid differentiation. Moreover tumor stage, size, and nuclear grade were all identified as independent predictors for both our cases and those from the SEER program. Patient groups with advanced RCC, and poorly differentiated RCC subgroups, were both determined to have a poor prognosis. The SSIGN model yielded the best predictive value as a prognostic model, followed by the Leibovich, and UCLA integrated staging system; this was the case for our patients, and for sub‐groups with a poor prognosis.

Conclusion

The prognosis of RCC was mostly influenced by tumor stage, size, and nuclear differentiation. SSIGN may represent the most suitable prognostic model for the Chinese population.