Clear cell renal cell carcinoma: validation of World Health Organization/International Society of Urological Pathology grading

Development and validation of intravoxel incoherent motion diffusion weighted imaging-based model for preoperative distinguishing nuclear grade and survival of clear cell renal cell carcinoma complicated with venous tumor thrombus

OBJECTIVE

To assess the utility of multiparametric MRI and clinical indicators in distinguishing nuclear grade and survival of clear cell renal cell carcinoma (ccRCC) complicated with venous tumor thrombus (VTT).

MATERIALS AND METHODS

This study included 105 and 27 patients in the training and test sets, respectively. Preoperative MRI, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), was performed. Renal lesions were evaluated for IVIM-DWI metrics and conventional MRI features. All the patients had postoperative histologically proven ccRCC and VTT. An expert uropathologist reviewed all specimens to confirm the nuclear grade of the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) of the tumor. Univariate and multivariable logistic regression analyses were used to select the preoperative imaging features and clinical indicators. The predictive ability of the logistic regression model was assessed using receiver operating characteristic (ROC) analysis. Survival curves were plotted using the Kaplan-Meier method.

RESULTS

High WHO/ISUP nuclear grade was confirmed in 69 of 105 patients (65.7%) in the training set and 19 of 27 patients (70.4%) in the test set, respectively (P = 0.647). Dp_ROI_Low, tumor size, serum albumin, platelet count, and lymphocyte count were independently related to high WHO/ISUP nuclear grade in the training set. The model identified high WHO/ISUP nuclear grade well, with an AUC of 0.817 (95% confidence interval [CI]: 0.735-0.899), a sensitivity of 70.0%, and a specificity of 77.8% in the training set. In the independent test set, the model demonstrated an AUC of 0.766 (95% CI, 0.567-0.966), a sensitivity of 79.0%, and a specificity of 75.0%. Kaplan-Meier analysis showed that the predicted high WHO/ISUP nuclear grade group had poorer progression-free survival than the low WHO/ISUP nuclear grade group in both the training and test sets (P = 0.001 and P = 0.021).

CONCLUSIONS

IVIM-DWI-derived parameters and clinical indicators can be used to differentiate nuclear grades and predict progression-free survival of ccRCC and VTT.

The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma

BACKGROUND

The aim is to develop and validate radiomics based on 2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) parameters for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC).

METHODS

A total of 209 patients with 214 lesions, who underwent 2-[18F]FDG PET/CT scans between December 2016 to December 2023, were included in our study. All ccRCC lesions were categorized into low grade (WHO/ISUP grade I-II) and high grade (WHO/ISUP grade III-IV). The lesions were allocated into a training group and a testing group in a ratio of 7:3. The radiomics features were extracted by a serious of maximum standardized uptake value (SUVmax) thresholds (0,2.5%,25%,40%) with the utilization of the minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithm. The clinical, radiomics and combined models were constructed. The receiver operating characteristic (ROC) curve, decision curve and calibration curves were plotted to assess the predicting performance.

RESULTS

The area under curve (AUC) of PET-0, PET-2.5%, PET-25%, PET-40% model in the training group were 0.881(95% CI: 0.822-0.940),0.883(95% CI: 0.825-0.942),0.889(95% CI: 0.831-0.946),0.887(95% CI: 0.826-0.948); and 0.878(95% CI: 0.777-0.978),0.876(95% CI: 0.776-0.977),0.871(95% CI: 0.769-0.972),0.882(95% CI: 0.786-0.979) in the testing group. Due to perfect prediction and verification performance, the volume of interest (VOI) from PET images with SUVmax threshold of 40% were selected to construct the radiomics model and combined model. The AUC of the clinical model and radiomics model was 0.859 (sensitivity = 0.846, specificity = 0.747) and 0.909 (sensitivity = 0.808, specificity = 0.751) in the training group, respectively; 0.882 (sensitivity = 0.857, specificity = 0.857) and 0.901 (sensitivity = 0.905, specificity = 0.833) in the testing group, respectively. In combined models, the AUC was 0.916, the sensitivity was 0.923 and the specificity was 0.808 in the training group; the AUC was 0.916, the sensitivity was 0.881 and the specificity was 0.792 in the training group.

CONCLUSION

Radiomics based on 2-[18F]FDG PET/CT can be helpful to predict WHO/ISUP grade of ccRCC.

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning

BACKGROUND

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy or surgery.

PURPOSE

CT imaging-based aggressiveness classification would be an important clinical advance, as it would facilitate non-invasive risk stratification and treatment planning. Here, we present a novel machine learning method, Correlated Feature Aggregation By Region (CorrFABR), for CT-based aggressiveness classification of ccRCC.

METHODS

CorrFABR is a multimodal fusion algorithm that learns from radiology and pathology images, and clinical variables in a clinically-relevant manner. CorrFABR leverages registration-independent radiology (CT) and pathology image correlations using features from vision transformer-based foundation models to facilitate aggressiveness assessment on CT images. CorrFABR consists of three main steps: (a) Feature aggregation where region-level features are extracted from radiology and pathology images at widely varying image resolutions, (b) Fusion where radiology features correlated with pathology features (pathology-informed CT biomarkers) are learned, and (c) Classification where the learned pathology-informed CT biomarkers, together with clinical variables of tumor diameter, gender, and age, are used to distinguish aggressive from indolent ccRCC using multi-layer perceptron-based classifiers. Pathology images are only required in the first two steps of CorrFABR, and are not required in the prediction module. Therefore, CorrFABR integrates information from CT images, pathology images, and clinical variables during training, but for inference, it relies solely on CT images and clinical variables, ensuring its clinical applicability. CorrFABR was trained with heterogenous, publicly-available data from 298 ccRCC tumors (136 indolent tumors, 162 aggressive tumors) in a five-fold cross-validation setup and evaluated on an independent test set of 74 tumors with a balanced distribution of aggressive and indolent tumors. Ablation studies were performed to test the utility of each component of CorrFABR.

RESULTS

CorrFABR outperformed the other classification methods, achieving an ROC-AUC (area under the curve) of 0.855 ± 0.0005 (95% confidence interval: 0.775, 0.947), F1-score of 0.793 ± 0.029, sensitivity of 0.741 ± 0.058, and specificity of 0.876 ± 0.032 in classifying ccRCC as aggressive or indolent subtypes. It was found that pathology-informed CT biomarkers learned through registration-independent correlation learning improves classification performance over using CT features alone, irrespective of the kind of features or the classification model used. Tumor diameter, gender, and age provide complementary clinical information, and integrating pathology-informed CT biomarkers with these clinical variables further improves performance.

CONCLUSION

CorrFABR provides a novel method for CT-based aggressiveness classification of ccRCC by enabling the identification of pathology-informed CT biomarkers, and integrating them with clinical variables. CorrFABR enables learning of these pathology-informed CT biomarkers through a novel registration-independent correlation learning module that considers unaligned radiology and pathology images at widely varying image resolutions.

Renal Cell Carcinoma: A Review

Importance

Renal cell carcinoma (RCC) is a common malignancy, with an estimated 434 840 incident cases worldwide in 2022. In the US, it is the sixth most common cancer among males and ninth among females.

Observations

Clear cell RCC is the most common histologic subtype (75%-80% of cases) and is characterized by inactivation of the von Hippel Lindau (VHL) tumor suppressor gene. Many patients (37%-61%) are diagnosed with RCC incidentally on an abdominal imaging study such as ultrasound or computed tomographic scan, and 70% of patients have stage I RCC at diagnosis. Although its incidence has increased approximately 1% per year from 2015 through 2019, the mortality rate of RCC has declined about 2% per year in the US from 2016 through 2020. Patients with a solid renal mass or complex cystic renal mass should be referred to urology. Treatment options for RCC confined to the kidney include surgical resection with partial or radical nephrectomy, ablative techniques (eg, cryoablation, radiofrequency ablation, radiation), or active surveillance for some patients (especially those with renal masses <2 cm). For patients with renal masses less than 4 cm in size (48% of patients), partial nephrectomy can result in a 5-year cancer-specific survival of more than 94%. For advanced or metastatic RCC, combinations of immune checkpoint inhibitors or the combination of immune checkpoint inhibitors with tyrosine kinase inhibitors are associated with tumor response of 42% to 71%, with a median overall survival of 46 to 56 months.

Conclusions and Relevance

RCC is a common malignancy that is often diagnosed incidentally on an abdominal imaging study. Seventy percent of patients are diagnosed with stage I RCC and 11% of patients with stage IV. First-line treatments for early-stage RCC are partial or radical nephrectomy, which can result in 5-year cancer-specific survival of more than 94%, ablative techniques, or active surveillance. New treatment options for patients with metastatic RCC include immune checkpoint inhibitors and tyrosine kinase inhibitors.

Same Organ, Two Cancers: Complete Analysis of Renal Cell Carcinomas and Upper Tract Urothelial Carcinomas

Background and Objectives: Renal cell carcinomas and upper tract urothelial carcinomas are types of malignancies that originate in the kidneys. Each of these examples shows an increasing trend in the frequency and the mortality rate. This study aims to comprehensively define carcinomas by analyzing clinical, paraclinical, and histological aspects to predict aggressiveness and mortality. Materials and Methods: We conducted a retrospective investigation on a group of patients suspected of kidney cancers. Results: We identified 188 cases. We observed a higher mortality rate and older age in individuals with urothelial carcinomas. Anemia, acute kidney injury, hematuria, and perineural invasion were the main risk factors that predicted their mortality. Tumor size in renal cell carcinomas correlates with the presence of necrosis and sarcomatoid areas. Factors that indicate a higher rate of death are older age, exceeding the renal capsule, a lesion that includes the entire kidney, lymphovascular invasion, acute kidney injury, and anemia. Conclusions: Even if they originate at the renal level, and the clinical-paraclinical picture is similar, the histopathological characteristics make the difference. In addition, to these are added the previously mentioned common parameters that can represent important prognostic factors. In conclusion, the characteristics commonly identified in one type of cancer may act as risk factors for the other tumor. The detected data include threshold values and risk factors, making a significant contribution to the existing literature.

Clinical outcome predictors for metastatic renal cell carcinoma: a retrospective multicenter real-life case series

Over the last decades, the therapeutic armamentarium of metastatic renal cell carcinoma (mRCC) has been revolutionized by the advent of tyrosin-kinase inhibitors (TKI), immune-checkpoint inhibitors (ICI), and immune-combinations. RCC is heterogeneous, and even the most used validated prognostic systems, fail to describe its evolution in real-life scenarios. Our aim is to identify potential easily-accessible clinical factors and design a disease course prediction system. Medical records of 453 patients with mRCC receiving sequential systemic therapy in two high-volume oncological centres were reviewed. The Kaplan-Meier method and Cox proportional hazard model were used to estimate and compare survival between groups. As first-line treatment 366 patients received TKI monotherapy and 64 patients received ICI, alone or in combination. The mean number of therapy lines was 2.5. A high Systemic Inflammation Index, a BMI under 25 Kg/m2, the presence of bone metastases before systemic therapy start, age over 65 years at the first diagnosis, non-clear-cell histology and sarcomatoid component were correlated with a worse OS. No significant OS difference was observed between patients receiving combination therapies and those receiving exclusively monotherapies in the treatment sequence. Our relapse prediction system based on pathological stage and histological grade was effective in predicting the time between nephrectomy and systemic treatment. Our multicentric retrospective analysis reveals additional potential prognostic factors for mRCC, not included in current validated prognostic systems, suggests a model for disease course prediction and describes the outcomes of the most common therapeutic strategies currently available.

Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study

OBJECTIVES

Accurate preoperative prediction of the pathological grade of clear cell renal cell carcinoma (ccRCC) is crucial for optimal treatment planning and patient outcomes. This study aims to develop and validate a deep-learning (DL) algorithm to automatically segment renal tumours, kidneys, and perirenal adipose tissue (PRAT) from computed tomography (CT) images and extract radiomics features to predict the pathological grade of ccRCC.

METHODS

In this cross-ethnic retrospective study, a total of 614 patients were divided into a training set (383 patients from the local hospital), an internal validation set (88 patients from the local hospital), and an external validation set (143 patients from the public dataset). A two-dimensional TransUNet-based DL model combined with the train-while-annotation method was trained for automatic volumetric segmentation of renal tumours, kidneys, and visceral adipose tissue (VAT) on images from two groups of datasets. PRAT was extracted using a dilation algorithm by calculating voxels of VAT surrounding the kidneys. Radiomics features were subsequently extracted from three regions of interest of CT images, adopting multiple filtering strategies. The least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and the support vector machine (SVM) for developing the pathological grading model. Ensemble learning was used for imbalanced data classification. Performance evaluation included the Dice coefficient for segmentation and metrics such as accuracy and area under curve (AUC) for classification. The WHO/International Society of Urological Pathology (ISUP) grading models were finally interpreted and visualized using the SHapley Additive exPlanations (SHAP) method.

RESULTS

For automatic segmentation, the mean Dice coefficient achieved 0.836 for renal tumours and 0.967 for VAT on the internal validation dataset. For WHO/ISUP grading, a model built with features of PRAT achieved a moderate AUC of 0.711 (95% CI, 0.604-0.802) in the internal validation set, coupled with a sensitivity of 0.400 and a specificity of 0.781. While model built with combination features of the renal tumour, kidney, and PRAT showed an AUC of 0.814 (95% CI, 0.717-0.889) in the internal validation set, with a sensitivity of 0.800 and a specificity of 0.753, significantly higher than the model built with features solely from tumour lesion (0.760; 95% CI, 0.657-0.845), with a sensitivity of 0.533 and a specificity of 0.767.

CONCLUSION

Automated segmentation of kidneys and visceral adipose tissue (VAT) through TransUNet combined with a conventional image morphology processing algorithm offers a standardized approach to extract PRAT with high reproducibility. The radiomics features of PRAT and tumour lesions, along with machine learning, accurately predict the pathological grade of ccRCC and reveal the incremental significance of PRAT in this prediction.

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma

OBJECTIVES

This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs).

METHODS

CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation.

RESULTS

There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76).

CONCLUSIONS

Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC.

ADVANCES IN KNOWLEDGE

This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity

BACKGROUND

Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management.

OBJECTIVES

The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group.

METHODS

Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours.

RESULTS

For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively.

CONCLUSIONS

Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Understanding Factors that Influence Prognosis and Response to Therapy in Clear Cell Renal Cell Carcinoma

In this review, we highlight and contextualize emerging morphologic prognostic and predictive factors in renal cell carcinoma. We focus on clear cell renal cell carcinoma (ccRCC), the most common histologic subtype. Our understanding of the molecular characterization of ccRCC has dramatically improved in the last decade. Herein, we highlight how these discoveries have laid the foundation for new approaches to prognosis and therapeutic decision-making for patients with ccRCC. We explore the clinical relevance of common mutations, established gene expression signatures, intratumoral heterogeneity, sarcomatoid/rhabdoid morphology and PD-L1 expression, and discuss their impact on predicting response to therapy.

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

BACKGROUND

We aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model.

METHODS

We conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis.

RESULTS

Of the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve.

CONCLUSIONS

Our research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.

Evaluation of contrast-enhanced ultrasound for predicting tumor grade in small (≤4 cm) clear cell renal cell carcinoma: Qualitative and quantitative analysis

OBJECTIVE

The study aimed to evaluate the utility of qualitative and quantitative analysis employing contrast-enhanced ultrasound (CEUS) in predicting the WHO/ISUP grade of small (≤4 cm) clear cell renal cell carcinoma (ccRCCs).

METHODS

Patients with small ccRCCs, confirmed by histological examination, underwent preoperative CEUS and were classified into low- (grade I/II) and high-grade (grade III/IV) groups. Qualitative and quantitative assessments of CEUS were conducted and compared between the two groups. Diagnostic performance was assessed using receiver operating characteristic curves.

RESULTS

A total of 72 patients were diagnosed with small ccRCCs, comprising 23 individuals in the high-grade group and 49 in the low-grade group. The low-grade group exhibited a significantly greater percentage of hyper-enhancement compared to the high-grade group (79.6% VS 39.1%, P < 0.05). The low-grade group showed significantly higher relative index values for peak enhancement, wash-in area under the curve, wash-in rate, wash-in perfusion index, and wash-out rate compared to the high-grade group (all P < 0.05). The AUC values for qualitative and quantitative parameters in predicting the WHO/ISUP grade of small ccRCCs ranged from 0.676 to 0.756.

CONCLUSIONS

Both qualitative and quantitative CEUS analysis could help to distinguish the high- from low-grade small ccRCCs.

[68Ga]Ga‑LNC1007 PET/CT in the evaluation of renal cell carcinoma: comparison with 2-[18F]FDG/[68Ga]Ga-PSMA PET/CT

PURPOSE

To compare the potential efficiency of [68Ga]Ga-LNC1007 with 2-[18F]FDG/[68Ga]Ga-PSMA PET/CT for detecting renal cell carcinoma (RCC) and to explore parameters derived from [68Ga]Ga-LNC1007 PET/CT for discriminating pathological characteristics in RCC.

METHODS

Twenty-five RCC patients confirmed by pathology were enrolled in this prospective study. The maximum standardized uptake value (SUVmax), mean SUV (SUVmean), gross tumor volume (GTV) and total lesion-tracer (TL-tracer) of lesions were calculated from the corresponding PET/CT images. Pathological characteristics included World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and adverse pathological features (tumor necrosis or sarcomatoid or rhabdoid feature).

RESULTS

[68Ga]Ga-LNC1007 PET/CT showed a higher detection rate for primary lesions than 2-[18F]FDG and [68Ga]Ga-PSMA (LNC1007 vs. FDG: 13/17 vs. 4/17, P = 0.005; LNC1007 vs. PSMA: 9/11 vs. 6/11, P = 0.361). [68Ga]Ga-LNC1007 PET/CT showed higher SUVmax (6.6 vs. 3.7, P = 0.005), SUVmean (4.1 vs. 2.3, P = 0.001) and TBR (2.6 vs. 1.7, P = 0.011) compared with 2-[18F]FDG PET/CT, and it also showed higher TBR (2.9 vs. 0.5, P = 0.003), TBR-delay (2.8 vs. 0.3, P = 0.003), GTV (84.1 vs. 42.9, P = 0.003) and TL-tracer (442.7 vs. 235.8, P = 0.008) compared with [68Ga]Ga-PSMA PET/CT. SUVmax and TBR derived from [68Ga]Ga-LNC1007 PET/CT could effectively differentiate WHO/ISUP grade (3-4 vs. 1-2) and adverse pathological features (positive vs. negative) (SUVmax: AUC 0.81, P = 0.04; AUC 0.80, P = 0.033; TBR: AUC 0.84, P = 0.026; AUC 0.85, P = 0.014). The SUVmax was positively correlated with the FAP expression, integrin αvβ3 expression and the total expression of FAP and integrin αvβ3 (r = 0.577, P = 0.006, r = 0.701, P < 0.001, and r = 0.702, P < 0.001, respectively).

CONCLUSION

[68Ga]Ga-LNC1007 is a promising tracer for RCC imaging and can effectively identify aggressive pathological characteristics of RCC.

Collateral vessels on preoperative enhanced computed tomography for predicting pathological grade of clear cell renal cell carcinoma: A retrospective study

OBJECTIVES

To retrospectively evaluate the association between the presence of collateral vessels and grade of clear cell renal cell carcinoma (ccRCC) and whether the presence of collateral vessels could serve as a predictor to differentiate high- and low-grade ccRCC.

MATERIALS AND METHODS

From May 2018 to September 2022, a total of 160 ccRCC patients with pathological diagnosis were enrolled in this study. Patients were divided into a high-grade group and a low-grade group according to World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system. The significant variables were extracted based on the univariate analyses using Student t test, Mann-Whitney U test, Chi-square test or Fisher's exact test. Multivariate logistic regression analyses were performed to determine independent factors among extracted variables. We calculated the sensitivity, specificity and their 95% confidence intervals (CI) of collateral vessels for predicting high WHO/ISUP grade to quantify its predictive performance. Furthermore, to investigate the additional predictive contribution of collateral vessels, a primary model and a control model were constructed to predict WHO/ISUP grade. The primary model included all extracted significant variables and the control model included significant variables except collateral vessels.

RESULTS

The proportion of ccRCC patients with collateral vessels was significantly larger in high-grade ccRCC than those in low-grade ccRCC (87.5 % vs. 26.8 %, P < 0.001). Multivariate logistic regression analyses showed that the presence of collateral vessels was an independent predictor for high WHO/ISUP grade (P < 0.001). The sensitivity and specificity of the presence of collateral vessels for differentiating high- and low-grade ccRCC were 87.5 % (95 % CI 0.753-0.941) and 73.2 % (95 % CI 0.643-0.806) respectively. Including collateral vessels in predictive model improves predictive performance for WHO/ISUP grade, increasing the area under the curve (AUC) value from 0.889 to 0.914.

CONCLUSION

The presence of collateral vessels has high sensitivity and specificity for differentiating high- and low-grade ccRCC and can improve the predictive performance for high WHO/ISUP grade.

A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma

OBJECTIVES

This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell's concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration.

RESULTS

The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912-0.980) and 0.864 (95% CI: 0.734-0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group.

CONCLUSIONS

The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC.

CRITICAL RELEVANCE STATEMENT

We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades.

KEY POINTS

1. The multi-model achieved an AUC of 0.864 for assessing pathological grade. 2. The multi-model exhibited an association with survival in ccRCC patients. 3. The high-grade group demonstrated a greater abundance of immune cells.

Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis

Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives.

The Role of the Pathologist in Renal Cell Carcinoma Management

Recent advances in our understanding of the molecular alterations underlying different types of renal cell carcinoma (RCC), as well as the implementation of immune checkpoint inhibitors in the treatment of patients with advanced disease, have significantly expanded the role of pathologists in the management of RCC patients and in the identification of predictive biomarkers that can guide patient treatment. In this chapter, we examine pathologists' evolving role in patient care and the development of precision medicine strategies for RCC.

Prediction of World Health Organization /International Society of Urological Pathology (WHO/ISUP) Pathological Grading of Clear Cell Renal Cell Carcinoma by Dual-Layer Spectral CT

RATIONALE AND OBJECTIVES

To evaluate whether the dual-layer spectral computed tomography urography (DL-CTU) images could predict WHO/ISUP pathological grading of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We retrospectively included patients (n = 50) with pathologically confirmed ccRCC who underwent preoperative DL-CTU (from October 2017 to February 2021). They were divided into low-grade (WHO/ISUP 1/2, n = 30) and high-grade groups (WHO/ISUP 3/4, n = 20). The lesion size, attenuation (HU), iodine concentration (IC), normalized IC(NIC), and other quantitative characteristics were compared between the two groups. HU, IC, and NIC were obtained by plotting ROI with two different methods (circular ROI in the solid component or irregular ROI along the tumor edge containing tumor necrotic components). Receiver operating characteristic curves and multivariable model were used to evaluate the ability of parameters to predict WHO/ISUP grade.

RESULTS

Transverse diameter (TD) of low-grade tumors was smaller, and HU in the non-contrast phase of the second method (HU-U-2) was lower than that of high-grade tumors (34.21±15.14 mm vs. 46.50 ± 20.68 mm, 27.33 ± 6.65 HU vs. 31.36 ± 6.09 HU, p< 0.05). The NIC in the nephrographic phase by the two methods (NIC-N-1 and NIC-N-2) of low-grade was higher than that of the high-grade group (0.78± 0.19 vs.0.58 ± 0.22, 0.73 ± 0.42 vs. 0.46 ± 0.22, p< 0.05). The final multivariable model composed of TD, HU-U-2, and NIC-N-1 could predict ccRCC grade with the area under the curve, sensitivity, specificity, and accuracy of 0.852, 70%, 90%, and 82%.

CONCLUSION

Quantitative indicators in DL-CTU images could help predict the WHO/ISUP grade of ccRCC.

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n = 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Our work reveals spatially interacting tumor-immune structures underlying ccRCC biology that may also inform selective response to ICI.

Prognostic factors in renal cell carcinoma: A single‑center study

Renal cell carcinoma (RCC) is a heterogeneous and complex disease with numerous pathophysiologic variants. ~40% of patients succumb due to the progression of the disease, making RCC the most fatal of the common urologic malignancies. Prognostic factors are indicators of the progression of the disease, and the precise determination of these factors is important for evaluating and managing RCC. In the present study, it was aimed to determine and find associations among the histopathological features of RCCs and their impact on survival and metastasis. This is a cross-sectional study of RCC cases who have undergone partial or radical nephrectomy from March 2008 to October 2021 and have been pathologically reviewed at Shorsh General Teaching Hospital in Sulaimani, Iraq. The data in the pathology studies were supplemented by follow-up of the patients to obtain information about survival, recurrence and metastasis. In total, 228 cases of RCC were identified, among whom 60.5% were men and 39.5% were women, with a median age of 51 years. The main tumor types were clear cell RCC (71.1%), papillary RCC (13.6%), and chromophobe RCC (11%). Various measures of aggressiveness, including tumor necrosis, sarcomatoid change, microvascular invasion, and parameters of invasiveness (invasion of the renal sinus and other structures), were significantly correlated with each other, and they were also associated with reduced overall survival and an increased risk of metastasis on univariate analysis. However, on multivariate analysis, only tumor size and grade, and microvascular invasion retained statistical significance and were associated with a lower survival rate. In conclusion, pathological parameters have an impact on prognosis in RCC. The most consistent prognostic factors can be tumor size and grade, and microvascular invasion.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma

Objective

This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC).

Methods

A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039).

Conclusion

The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Factors affecting survival and prognosis in surgically treated patients with spinal metastases arising from renal cell carcinoma

BACKGROUND

The objective of this study was to explore the prognostic factors for renal cell carcinoma (RCC) patients with spinal metastasis who underwent surgical treatment in our hospital.

METHODS

We retrospectively analyzed the clinical data and survival status of 49 patients with spinal metastases arising from RCC. All patients with spinal metastases underwent surgical treatment. We analyzed a range of factors that may affect the prognosis of patients with RCC. Using Kaplan-Meier method to perform univariate analysis of the factors that might affect spine metastasis free survival (SMFS)and survival after spinal metastasis (OS) respectively. Establish Cox proportional hazards model to extract independent prognostic factors for SMFS and OS.

RESULTS

The mean time of SMFS was 27 months (median 8, range 0-180 months). The mean time of OS was 12.04 months (median 9, range 2-36 months). RCC with visceral metastasis (p = 0.001,HR 11.245,95%CI 2.824-44.776) and AJCC RCC Stage (p = 0.040,HR 2.809,95%CI 1.046-7.543) can significantly affect SMFS. Furthermore, WHO/ISUP Grade (p < 0.001, HR 2.787,95%CI 1.595-4.870), ECOG Score (p = 0.019, HR 0.305,95%CI 0.113-0.825) and multiple spinal metastases (p < 0.001, HR 0.077,95%CI 0.019-0.319) have significant effects on OS.

CONCLUSIONS

RCC with visceral metastasis and AJCC RCC Stage were independent prognostic factors for SMFS. WHO/ISUP Grade, ECOG Scores and multiple spinal metastases were independent prognostic factors for OS.

Pre-operative Prediction of Invasiveness in Renal Cell Carcinoma: The Role of Conventional Ultrasound and Contrast-Enhanced Ultrasound

OBJECTIVE

It is known that in patients with renal cell carcinoma (RCC), the invasiveness of the tumor is closely related to the treatment and prognosis. Currently, histologic diagnosis of RCC is typically established after surgical removal of tumors or after biopsy. The use of non-invasive imaging modalities to predict the invasiveness of RCC is of great clinical value, particularly before surgery. In this study, the differences in conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) features between invasive and non-invasive RCC were analyzed with the aim of providing more accurate and valuable information for diagnosis and treatment to clinically optimize the treatment plan in a non-invasive manner and improve the prognosis of patients.

METHODS

Conventional US and CEUS features of 163 patients (total of 164 RCCs), obtained from the Lanzhou University Second Hospital in the period ranging from March 2021 to September 2022, were retrospectively analyzed. Patients were categorized into two groups: invasive group (n = 44) and non-invasive group (n = 120), with surgical pathology as reference standard. Receiver operating characteristic curves were drawn to evaluate the feasibility of differentiation.

RESULTS

The possibility of an intrarenal lesion/kidney ratio >50% in the invasive group (13/44, 29.5%) was significantly higher than that in the non-invasive group (8/120, 6.7%) (p < 0.001). The absence of perilesional rim-like enhancement was more likely to imply invasive RCC (30/44, 68.2%) than non-invasive RCC (100/120, 83.3%) (p = 0.049) and was an independent predictor of invasive RCC. As for CEUS quantitative features, there were statistically significant differences in peak intensity (p = 0.009) or peak enhancement (p = 0.010), taking the largest range of lesion as the region of interest.

CONCLUSION

Conventional US and CEUS features may help in the differentiation of invasive RCC from non-invasive RCC and have potential application value in the pre-operative prediction of RCC invasiveness.

Diagnostic Value of Contrast-Enhanced Ultrasound Features for WHO/ISUP Grading in Renal Cell Carcinoma

OBJECTIVES

By analyzing the differences of contrast-enhanced ultrasound (CEUS) features between low- and high-grade of WHO/ISUP grading, to explore the diagnostic value of CEUS in evaluating the prognosis of renal cell carcinoma (RCC).

METHODS

The qualitative and quantitative features of CEUS in 69 patients with RCC confirmed by surgical pathology in the Lanzhou University Second Hospital from March to October 2021 were retrospectively analyzed. Patients were categorized into two groups: low-grade group (n = 22) and high-grade group (n = 47), with surgical pathology as reference standard. The diagnostic performance of statistically significant CEUS features was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

There were statistically significant differences in enhancement degree (P = .032) and quantitative features such as slope_lesion (P = .034), the differences between lesion and cortex in arrive time (∆AT = AT_lesion  - AT_cortex , P = .013), peak intensity(∆PI = [PI_lesion  - PI_cortex ]/PI_cortex , P = .003), area under the curve (∆Area = Area_lesion  - Area_cortex , P = .008) in two groups, and the sensitivity was 70.2% and specificity was 71.4% of ∆PI, which has a high diagnostic performance in the differentiation of low-grade group from high-grade group (P = .005).

CONCLUSIONS

CEUS features such as ∆PI, may help differentiate low-grade RCC from high-grade RCC. CEUS has a promising application prospect in preoperative evaluation of the prognosis of RCC.

An integrative multi-omics analysis based on disulfidptosis-related prognostic signature and distinct subtypes of clear cell renal cell carcinoma

Background

The association between clear cell renal cell carcinoma (ccRCC) and disulfidoptosis remains to be thoroughly investigated.

Methods

We conducted multiple bioinformatics analyses, including prognostic analysis and cluster analysis, using R software. Additionally, we utilized Quantitative Real-time PCR to measure RNA levels of specific genes. The proliferation of ccRCC was assessed through CCK8 and colony formation assays, while the invasion and migration of ccRCC cells were evaluated using the transwell assay.

Results

In this study, utilizing data from multiple ccRCC cohorts, we identified molecules that contribute to disulfidoptosis. We conducted a comprehensive investigation into the prognostic and immunological roles of these molecules. Among the disulfidoptosis-related metabolism genes (DMGs), LRPPRC, OXSM, GYS1, and SLC7A11 exhibited significant correlations with ccRCC patient prognosis. Based on our signature, patients in different groups displayed varying levels of immune infiltration and different mutation profiles. Furthermore, we classified patients into two clusters and identified multiple functional pathways that play important roles in the occurrence and development of ccRCC. Given its critical role in disulfidoptosis, we conducted further analysis on SLC7A11. Our results demonstrated that ccRCC cells with high expression of SLC7A11 exhibited a malignant phenotype.

Conclusions

These findings enhanced our understanding of the underlying function of DMGs in ccRCC.

PI3K/AKT/mTOR Dysregulation and Reprogramming Metabolic Pathways in Renal Cancer: Crosstalk with the VHL/HIF Axis

Renal cell carcinoma (RCC) represents 85-95% of kidney cancers and is the most frequent type of renal cancer in adult patients. It accounts for 3% of all cancer cases and is in 7th place among the most frequent histological types of cancer. Clear cell renal cell carcinoma (ccRCC), accounts for 75% of RCCs and has the most kidney cancer-related deaths. One-third of the patients with ccRCC develop metastases. Renal cancer presents cellular alterations in sugars, lipids, amino acids, and nucleic acid metabolism. RCC is characterized by several metabolic dysregulations including oxygen sensing (VHL/HIF pathway), glucose transporters (GLUT 1 and GLUT 4) energy sensing, and energy nutrient sensing cascade. Metabolic reprogramming represents an important characteristic of the cancer cells to survive in nutrient and oxygen-deprived environments, to proliferate and metastasize in different body sites. The phosphoinositide 3-kinase-AKT-mammalian target of the rapamycin (PI3K/AKT/mTOR) signaling pathway is usually dysregulated in various cancer types including renal cancer. This molecular pathway is frequently correlated with tumor growth and survival. The main aim of this review is to present renal cancer types, dysregulation of PI3K/AKT/mTOR signaling pathway members, crosstalk with VHL/HIF axis, and carbohydrates, lipids, and amino acid alterations.

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). Established histopathology paradigms like nuclear grade have baseline prognostic relevance for ccRCC, although whether existing or novel histologic features encode additional heterogeneous biological and clinical states in ccRCC is uncertain. Here, we developed spatially aware deep learning models of tumor- and immune-related features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSI) in untreated and treated contexts (n = 1102 patients). We discovered patterns of nuclear grade heterogeneity in WSI not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associated with PBRM1 loss of function, adverse clinical factors, and selective patient response to ICI. Joint computer vision analysis of tumor phenotypes with inferred tumor infiltrating lymphocyte density identified a further subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associated with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Thus, our work reveals novel spatially interacting tumor-immune structures underlying ccRCC biology that can also inform selective response to ICI.

Etiologies, Gross Appearance, Histopathological Patterns, Prognosis, and Best Treatments for Subtypes of Renal Carcinoma: An Educational Review

Of all primary renal neoplasms, 80-85% are renal cell carcinomas (RCCs), which develop in the renal cortex. There are more than 10 histological and molecular subtypes of the disease, the most frequent of which is clear cell RCC, which also causes most cancer-related deaths. Other renal neoplasms, including urothelial carcinoma, Wilms' tumor, and renal sarcoma, each affect a particular age group and have specific gross and histological features. Due to the genetic susceptibility of each of these malignancies, early mutation discovery is necessary for the early detection of a tumor. Furthermore, it is crucial to avoid environmental factors leading to each type. This study provides relatively detailed and essential information regarding each subtype of renal carcinoma.

Hot topics in renal cancer pathology: implications for clinical management

INTRODUCTION

The updated European Association of Urology (EAU) Guidelines issued a weak recommendation for adjuvant pembrolizumab for patients with high-risk operable clear cell Renal Cell Carcinoma (ccRCC). High risk of recurrence was defined, as per protocol-criteria, as T2 with nuclear grade 4 or sarcomatoid differentiation, T3 or higher, regional lymph node metastasis, or stage M1 with no evidence of disease. Considering the heterogeneous population included in the recommendation, it has been questioned if adjuvant pembrolizumab may lead to overtreatment of some patients as well as undertreatment of patients with worse prognosis.

AREAS COVERED

In this review, we discuss the issues related to the assessment of pathological features required to identify those patients harboring a high-risk tumor, highlighting the issue related to interobserver variability and discuss the currently available prognostic scoring systems in ccRCC.

EXPERT OPINION

PPathologist assessment of prognostic features suffers from interobserver variability which may depend on gross sampling and the pathologist's expertise. The presence of clear cell feature is not sufficient criteria by itself to define ccRCC since clear cell can be also found in other histotypes. Application of molecular biomarkers may be useful tools in the near future to help clinicians identify patients harboring tumors with worse prognosis.

An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma

Objectives

To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM).

Methods

175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test.

Results

The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p &lt;0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p &lt; 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration.

Conclusion

The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.

Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading

Purpose: To evaluate the usefulness of computed tomography (CT) texture descriptors integrated with machine-learning (ML) models in the identification of clear cell renal cell carcinoma (ccRCC) and for the first time papillary renal cell carcinoma (pRCC) tumor nuclear grades [World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) 1, 2, 3, and 4]. Approach: A total of 143 ccRCC and 21 pRCC patients were analyzed in this study. Texture features were extracted from late arterial phase CT images. A complete separation of training/validation and testing subsets from the beginning to the end of the pipeline was adopted. Feature dimension was reduced by collinearity analysis and Gini impurity-based feature selection. The synthetic minority over-sampling technique was employed for imbalanced datasets. The ML classifiers were logistic regression, SVM, RF, multi-layer perceptron, and K -NN. The differentiation between low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and between all grades was assessed for ccRCC and pRCC datasets. The classification performance was assessed and compared by certain metrics. Results: Textures-based classifiers were able to efficiently identify ccRCC and pRCC grades. An accuracy and area under the characteristic operating curve (AUC) up to 91%/0.9, 91%/0.9, 90%/0.9, and 88%/1 were reached when discriminating ccRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. An accuracy and AUC up to 96%/1, 81%/0.8, 86%/0.9, and 88%/0.9 were found when differentiating pRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. Conclusion: CT texture-based ML models can be used to assist radiologist in predicting the WHO/ISUP grade of ccRCC and pRCC pre-operatively.

The Influence of Oncogenic Viruses in Renal Carcinogenesis: Pros and Cons

Viral infections are major contributors to the global cancer burden. Recent advances have revealed that known oncogenic viruses promote carcinogenesis through shared host cell targets and pathways. The aim of this review is to point out the connection between several oncogenic viruses from the Polyomaviridae, Herpesviridae and Flaviviridae families and renal carcinogenesis, highlighting their involvement in the carcinogenic mechanism. We performed a systematic search of the PubMed and EMBASE databases, which was carried out for all the published studies on RCC in the last 10 years, using the following search algorithm: renal cell carcinoma (RCC) and urothelial carcinoma, and oncogenic viruses (BKPyV, EBV, HCV, HPV and Kaposi Sarcoma Virus), RCC and biomarkers, immunohistochemistry (IHC). Our analysis included studies that were published in English from the 1st of January 2012 to the 1st of May 2022 and that described and analyzed the assays used for the detection of oncogenic viruses in RCC and urothelial carcinoma. The virus most frequently associated with RCC was BKPyV. This review of the literature will help to understand the pathogenic mechanism of the main type of renal malignancy and whether the viral etiology can be confirmed, at a minimum, as a co-factor. In consequence, these data can contribute to the development of new therapeutic strategies. A virus-induced tumor could be efficiently prevented by vaccination or treatment with oncolytic viral therapy and/or by targeted therapy.

Overexpression of cytoplasmic dynamin 2 is associated with worse outcomes in patients with clear cell renal cell carcinoma

BACKGROUND: Dynamin 2 (DNM2) involved in tumor progression in various malignancies. OBJECTIVE: For the first time, we evaluated DNM2 expression pattern, its association with clinicopathological characteristics and survival outcomes in RCC subtypes. METHODS: We evaluated the DNM2 expression pattern in RCC tissues as well as adjacent normal tissue using immunohistochemistry on tissue microarray (TMA) slides. RESULTS: Our findings revealed increased DNM2 expression in RCC samples rather than in adjacent normal tissues. The results indicated that there was a statistically significant difference between cytoplasmic expression of DNM2 among subtypes of RCC in terms of intensity of staining, percentage of positive tumor cells, and H-score (P= 0.024, 0.049, and 0.009, respectively). The analysis revealed that increased cytoplasmic expression of DNM2 in ccRCC is associated with worse OS (log rank: P= 0.045), DSS (P= 0.049), and PFS (P= 0.041). Furthermore, cytoplasmic expression of DNM2 was found as an independent prognostic factor affecting DSS and PFS in multivariate analysis. CONCLUSIONS: Our results indicated that DNM2 cytoplasmic expression is associated with tumor aggressiveness and poor outcomes. DNM2 could serve as a promising prognostic biomarker and therapeutic target in patients with ccRCC.

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

Simple Summary

Clear cell renal cell carcinoma (ccRCC) pathologic grade identification is essential to both monitoring patients’ conditions and constructing individualized subsequent treatment strategies. However, biopsies are typically used to obtain the pathological grade, entailing tremendous physical and mental suffering as well as heavy economic burden, not to mention the increased risk of complications. Our study explores a new way to provide grade assessment of ccRCC on the basis of the individual’s appearance on CT images. A deep learning (DL) method that includes self-supervised learning is constructed to identify patients with high grade for ccRCC. We confirmed that our grading network can accurately differentiate between different grades of CT scans of ccRCC patients using a cohort of 706 patients from West China Hospital. The promising diagnostic performance indicates that our DL framework is an effective, non-invasive and labor-saving method for decoding CT images, offering a valuable means for ccRCC grade stratification and individualized patient treatment.

Abstract

This retrospective study aimed to develop and validate deep-learning-based models for grading clear cell renal cell carcinoma (ccRCC) patients. A cohort enrolling 706 patients (n = 706) with pathologically verified ccRCC was used in this study. A temporal split was applied to verify our models: the first 83.9% of the cases (years 2010–2017) for development and the last 16.1% (year 2018–2019) for validation (development cohort: n = 592; validation cohort: n = 114). Here, we demonstrated a deep learning(DL) framework initialized by a self-supervised pre-training method, developed with the addition of mixed loss strategy and sample reweighting to identify patients with high grade for ccRCC. Four types of DL networks were developed separately and further combined with different weights for better prediction. The single DL model achieved up to an area under curve (AUC) of 0.864 in the validation cohort, while the ensembled model yielded the best predictive performance with an AUC of 0.882. These findings confirms that our DL approach performs either favorably or comparably in terms of grade assessment of ccRCC with biopsies whilst enjoying the non-invasive and labor-saving property.

Artificial Intelligence in Kidney Cancer

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

Renal Oncocytoma: The Diagnostic Challenge to Unmask the Double of Renal Cancer

Renal oncocytoma represents the most common type of benign neoplasm that is an increasing concern for urologists, oncologists, and nephrologists due to its difficult differential diagnosis and frequent overtreatment. It displays a variable neoplastic parenchymal and stromal architecture, and the defining cellular element is a large polygonal, granular, eosinophilic, mitochondria-rich cell known as an oncocyte. The real challenge in the oncocytoma treatment algorithm is related to the misdiagnosis due to its resemblance, at an initial radiological assessment, to malignant renal cancers with a completely different prognosis and medical treatment. Unfortunately, percutaneous renal biopsy is not frequently performed due to the possible side effects related to the procedure. Therefore, the majority of oncocytoma are diagnosed after the surgical operation via partial or radical nephrectomy. For this reason, new reliable strategies to solve this issue are needed. In our review, we will discuss the clinical implications of renal oncocytoma in daily clinical practice with a particular focus on the medical diagnosis and treatment and on the potential of novel promising molecular biomarkers such as circulating microRNAs to distinguish between a benign and a malignant lesion.

Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma-A Proof of Principle Study

Differentiating aggressive clear cell renal cell carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of renal tumors using carbon-13 MRI following injection of hyperpolarized [1-13C]pyruvate (HP-13C-MRI) and validated these findings with histopathology. Nine patients with treatment-naïve renal tumors (6 ccRCCs, 1 liposarcoma, 1 pheochromocytoma, 1 oncocytoma) underwent pre-operative HP-13C-MRI and conventional proton (1H) MRI. Multi-regional tissue samples were collected using patient-specific 3D-printed tumor molds for spatial registration between imaging and molecular analysis. The apparent exchange rate constant (kPL) between 13C-pyruvate and 13C-lactate was calculated. Immunohistochemistry for the pyruvate transporter (MCT1) from 44 multi-regional samples, as well as associations between MCT1 expression and outcome in the TCGA-KIRC dataset, were investigated. Increasing kPL in ccRCC was correlated with increasing overall tumor grade (ρ = 0.92, p = 0.009) and MCT1 expression (r = 0.89, p = 0.016), with similar results acquired from the multi-regional analysis. Conventional 1H-MRI parameters did not discriminate tumor grades. The correlation between MCT1 and ccRCC grade was confirmed within a TCGA dataset (p < 0.001), where MCT1 expression was a predictor of overall and disease-free survival. In conclusion, metabolic imaging using HP-13C-MRI differentiates tumor aggressiveness in ccRCC and correlates with the expression of MCT1, a predictor of survival. HP-13C-MRI may non-invasively characterize metabolic phenotypes within renal cancer.

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

Objective

This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).

Methods

370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results

Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.

Conclusion

The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

Nucleolar Proteins and Non-Coding RNAs: Roles in Renal Cancer

Renal cell cancer is the most frequent kidney malignancy. Most RCC cases are classified as clear cell renal cell carcinoma (ccRCC), characterized by high aggressiveness and poor prognosis for patients. ccRCC aggressiveness is defined by classification systems based on changes in morphology of nucleoli, the membraneless substructures of nuclei. The latter act as the sites of ribosome biogenesis as well as the hubs that trap and immobilize proteins, preventing their action in other cellular compartments. Thereby, nucleoli control cellular functioning and homeostasis. Nucleoli are also the sites of activity of multiple noncoding RNAs, including snoRNAs, IGS RNA, and miRNAs. Recent years have brought several remarkable discoveries regarding the role of nucleolar non-coding RNAs, in particular snoRNAs, in ccRCC. The expression of snoRNAs is largely dysregulated in ccRCC tumors. snoRNAs, such as SNHG1, SNHG4 and SNHG12, act as miRNA sponges, leading to aberrant expression of oncogenes and tumor suppressors, and directly contributing to ccRCC development and progression. snoRNAs can also act without affecting miRNA functioning, by altering the expression of key oncogenic proteins such as HIF1A. snoRNAs are also potentially useful biomarkers of ccRCC progression. Here, we comprehensively discuss the role of nucleolar proteins and non-coding RNAs in ccRCC.

Modified cancer TNM classification for localized renal cell carcinoma based on the prognostic analysis of 3748 cases from a single center

The optimal cutoff point for evaluating the prognosis of localized renal cell carcinoma (LRCC) remains unclear. This study aimed to verify the efficacy of tumor diameter in the 2010 American Joint Committee on Cancer (AJCC) TNM staging system and contribute to the modification of TNM staging on the prognosis of this disease. A total of 3748 patients with LRCC were enrolled and grouped according to the 2010 AJCC TNM staging system. COX analysis was used to stratify the prognosis. The optimal cutoff point of the tumor diameter in the T1 and T2 prognosis was explored. There were 3330 (88.9%) patients in stage T1 and 418 (11.1%) in stage T2. The cancer-specific mortality rate was 2.7% (100/3748). The mean follow-up was 49.8 months. A tumor diameter of 7 cm can determine the prognosis of patients at stages T1 and T2; however, 4.5 cm and 11 cm as the cutoff points for T1 and T2 sub-classification of patients with LRCC might show better recognition ability than 4 cm and 10 cm, respectively. The 2010 AJCC TNM stage can predict the prognosis of LRCC in stages T1 and T2. In addition, a tumor diameter of 4.5 cm and 11 cm might be the optimal cutoff points for the sub-classification of stages T1 and T2.

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis

OBJECTIVE

To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT.

METHODS

This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed.

RESULTS

Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI.

CONCLUSION

Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading.

KEY POINTS

• Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.

Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma?

OBJECTIVE

The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT).

MATERIALS AND METHODS

Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model.

RESULTS

Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05).

CONCLUSION

This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.

WHO/ISUP grading of clear cell renal cell carcinoma and papillary renal cell carcinoma; validation of grading on the digital pathology platform and perspectives on reproducibility of grade

Background

There are recognised potential pitfalls in digital diagnosis in urological pathology, including the grading of dysplasia. The World Health Organisation/International Society of Urological Pathology (WHO/ISUP) grading system for renal cell carcinoma (RCC) is prognostically important in clear cell RCC (CCRCC) and papillary RCC (PRCC), and is included in risk stratification scores for CCRCC, thus impacting on patient management. To date there are no systematic studies examining the concordance of WHO/ISUP grading between digital pathology (DP) and glass slide (GS) images. We present a validation study examining intraobserver agreement in WHO/ISUP grade of CCRCC and PRCC.

Methods

Fifty CCRCCs and 10 PRCCs were graded (WHO/ISUP system) by three specialist uropathologists on three separate occasions (DP once then two GS assessments; GS1 and GS2) separated by wash-out periods of at least two-weeks. The grade was recorded for each assessment, and compared using Cohen’s and Fleiss’s kappa.

Results

There was 65 to 78% concordance of WHO/ISUP grading on DP and GS1. Furthermore, for the individual pathologists, the comparative kappa scores for DP versus GS1, and GS1 versus GS2, were 0.70 and 0.70, 0.57 and 0.73, and 0.71 and 0.74, and with no apparent tendency to upgrade or downgrade on DP versus GS. The interobserver kappa agreement was less, at 0.58 on DP and 0.45 on GS.

Conclusion

Our results demonstrate that the assessment of WHO/ISUP grade on DP is noninferior to that on GS. There is an apparent slight improvement in agreement between pathologists on RCC grade when assessed on DP, which may warrant further study.

The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma

BACKGROUND

The Fuhrman nuclear grade system is one of the most important independent indicators in patients with clear cell renal cell carcinoma (ccRCC) for aggressiveness and prognosis. Preoperative assessment of tumor aggressiveness is important for surgical decision-making.

PURPOSE

To explore the role of magnetic resonance imaging (MRI) texture analysis based on susceptibility-weighted imaging (SWI) in predicting Fuhrman grade of ccRCC.

MATERIAL AND METHODS

A total of 45 patients with SWI and surgically proven ccRCC were divided into two groups: the low-grade group (Fuhrman I/II, n = 29) and the high-grade group (Fuhrman III/IV, n = 16). Texture features were extracted from SWI images. Feature selection was performed, and multivariable logistic regression analysis was performed to develop the SWI-based texture model for grading ccRCCs. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of the model.

RESULTS

A total of 396 SWI-based texture features were extracted from each SWI image. The SWI-based texture model developed by multivariable logistic regression analysis was: SWIscore = -0.59 + 1.60 * ZonePercentage. The area under the ROC curve of the SWI-based texture model for differentiating high-grade ccRCC from low-grade ccRCC was 0.81 (95% confidence interval 0.67-0.94), with 80% accuracy, 56.25% sensitivity, and 93.10% specificity. After 100 LGOCVs, the mean accuracy, sensitivity, and specificity were 90.91%, 91.83%, and 89.89% for the training sets, and 77.29%, 80.52%, and 71.44% for the test sets, respectively.

CONCLUSION

SWI-based texture analysis might be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.

Impact of Histology and Tumor Grade on Clinical Outcomes Beyond 5 Years of Follow-Up in a Large Cohort of Renal Cell Carcinomas

INTRODUCTION

The optimal length for clinical follow-up of renal cell carcinoma (RCC) patients is unclear. We evaluated the impact of ISUP/WHO tumor grade and histological subtype on short- and long-term survival and risk of recurrence/metastasis in a large cohort of RCC patients.

PATIENTS AND METHODS

We studied 1679 RCC patients from a single referral center in Italy. Adjusted hazard ratios for overall survival were estimated using Cox regression models. Adjusted absolute risk of developing recurrence or metastasis was computed considering competing risks of mortality.

RESULTS

During up to 13 years of follow-up, 175 (10.4%) RCC patients died, of whom 92% beyond 5 years. Hazard ratio of grade IV clear cell carcinomas (ccRCC) was 3.82 compared to grade II. Notably, 33% of recurrences and 56% of distant metastases occurred beyond 5 years of follow-up. The estimated probabilities of recurrence/metastasis were 15% and 45% within and beyond 5 years of follow-up, respectively. After 5 years, the absolute risk of recurrences increased also for papillary renal cell carcinoma type I (35.2%) and grade I ccRCC (17%).

CONCLUSION

After 5 years of follow-up, both risk of mortality and recurrences or metastases were high and were modified by histological types and tumor grade. These data strongly support histology- and grade-tailored surveillance strategies and long-term follow-up for RCC patients.

Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology

Background

There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear.

Method

TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. “GSVA” package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. “Survival” package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model.

Results

In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model’s genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway.

Conclusion subsections

These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08356-9.

Tumor Purity Coexpressed Genes Related to Immune Microenvironment and Clinical Outcomes of Lung Adenocarcinoma

Purpose

Lung cancer tissue includes tumor tissue, stromal cells, immune cells, and epithelial cells. These nontumor cells dilute the tumor purity in lung cancer tissues. Tumor purity plays an essential role in the immune response to lung cancer. At present, the biological processes related to the purity of lung cancer tumors remains unclear.

Methods

We measured tumor purity in 486 lung carcinoma tissues from TCGA-LUAD FPKM by using the "estimate" R package. Lung carcinoma tumor mutation burden was calculated by analyzing TCGA single nucleotide polymorphism data. The immune cell proportion was also evacuated via the CIBERSORT method. Lung carcinoma samples with P < 0.05 were considered significant. Based on the tumor purity and lung carcinoma gene matrix, we performed weighted gene coexpression network analysis (WGCNA), and the tumor purity-related module was identified. Then, we analyzed the functions of the factors involved in the module. We screened the coexpressed factors related to clinical outcome and immunophenotype. Finally, expression levels of these factors were measured at tissue and single-cell levels.

Results

A lung cancer tumor purity correlated coexpression network was determined. Five coexpressed genes (CD4, CD53, EVI2B, PLEK, and SASH3) were identified as tumor purity coexpressed genes that negatively correlated with tumor purity. Because the factors in the coexpression network often participate in similar biological processes, we found that CD4, CD53, EVI2B, PLEK, and SASH3 were most related to positive regulation of cytokine production and interleukin-2 production through functional enrichment. In a clinical phenotype analysis, we found that these five factors can be used as independent prognostic risk factors. We found that these factors were significantly negatively correlated with tumor purity and positively correlated with the immune score in the immunophenotyping analysis. Using GSEA analysis, we found that the antigen processing and presentation pathway were related to the five tumor coexpressed genes mentioned above. SASH3 and CD53 were used to conduct a prognostic model based on the interaction analysis of the Support Vector Machine and the Least Absolute Shrinkage and Selection Operator. SASH3 was verified to be related to CD8A using a single-cell analysis.

Conclusion

Tumor purity-related coexpression factors in the tumor microenvironment have essential clinical, genomic, and biological significance in lung cancer. These coexpression factors (SASH3 and CD53) can be used to classify tumor purity phenotypes and to predict clinical outcomes.