scholarly journals Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Subhanik Purkayastha ◽  
Yijun Zhao ◽  
Jing Wu ◽  
Rong Hu ◽  
Aidan McGirr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clinical Decision Making ◽  
External Validation ◽  
Automatic Machine ◽  
Low Grade ◽  
High Grade ◽  
Fuhrman Grade

Abstract Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I–II) from high-grade (Fuhrman III–IV) renal cell carcinoma using radiomics features extracted from routine MRI. 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 lesions with information on Fuhrman grade from 4 institutions were divided into training and test sets with an 8:2 split for model development and internal validation. Another 43 lesions from a separate institution were set aside for independent external validation. The performance of TPOT (Tree-Based Pipeline Optimization Tool), an automatic machine learning pipeline optimizer, was compared to hand-optimized machine learning pipeline. The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49–0.68), accuracy of 0.77 (95% CI 0.68–0.84), sensitivity of 0.38 (95% CI 0.29–0.48), and specificity of 0.86 (95% CI 0.78–0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50–0.69), accuracy of 0.81 (95% CI 0.72–0.88), sensitivity of 0.12 (95% CI 0.14–0.30), and specificity of 0.97 (95% CI 0.87–0.97). Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipeline on an external validation test non-invasively predicting Fuhrman grade of renal cell carcinoma using conventional MRI.

scholarly journals Immunohistochemical analysis of beta-catenin expression: a probable prognostic marker and potential therapeutic target in renal cell carcinoma

2021 ◽  
Author(s):  
Ayan Kundu ◽  
Anway Sen ◽  
Shouvik Choudhury ◽  
Tapan Kumar Mandal ◽  
Debasish Guha ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Tumor Stage ◽  
Low Grade ◽  
Beta Catenin ◽  
High Grade ◽  
Positive Correlation ◽  
Score Difference

Background and aims. Renal cell carcinoma (RCC) seems to be the most aggressive type of genitourinary neoplasm. Down regulation of normal beta-catenin expression contributes to development of RCC, reflecting the role of beta-catenin/Wnt signaling pathway in pathogenesis. This study aims to evaluate the significance of beta-catenin expression and its correlation with the prognostic parameters. Methods. A cross-sectional observational study was carried out in a tertiary care center on 58 RCC cases using variables like histological grade and type, tumor stage, necrosis. Formalin fixed, paraffin-embedded blocks were evaluated for beta-catenin expression by immunohistochemistry using scoring system. Data were analyzed by mean ± SD, χ2 test, Pearson’s correlation test. Results. Membranous score (MS) had a strong negative correlation with tumor stage (r = -0.407, p = 0.044) and grade (r = -0.787, p = <0.001). Mean membranous score difference between low (Stage 1 and 2) vs. high stage (Stage 3 and 4) and low (Grade 1 and 2) vs. high grade (Grade 3 and 4) was statistically significant (p < 0.001). Cytoplasmic score (CS) had positive correlation with tumor stage (r = 0.586; p = 0.002). No significant correlation was evident between cytoplasmic scores and tumor grade, however the mean cytoplasmic score difference between low grade vs. high grade was statistically significant (p < 0.001). Conclusion. Beta-catenin may play a crucial role in the pathogenesis of RCC and has a positive correlation with the biological behavior of this tumor. The important role of beta-catenin as a prognostic parameter and probably a critical evaluator of targeted chemotherapy cannot be overemphasized.

scholarly journals A case of high-grade mucinous tubular and spindle cell carcinoma

2020 ◽  
Vol 2020 (2) ◽  
Author(s):  
Koujin Miura ◽  
Yasushi Adachi ◽  
Toshiaki Shirahase ◽  
Yoji Nagashima ◽  
Kazuki Suemune ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Spindle Cell ◽  
Left Kidney ◽  
Spindle Cell Carcinoma ◽  
Low Grade ◽  
High Grade ◽  
Tomography Scan

Abstract Mucinous tubular and spindle cell carcinoma (MTSCC) is a rare renal cell carcinoma that initially presents as low-grade renal cell carcinoma. However, cases of MTSCC with high-grade histology and poor prognosis have been reported. Here, we report a case of MTSCC with high-grade histological features and metastasis. A 77-year-old woman consulted a hospital following frequent and painful micturition. Computed tomography scan revealed a tumor of the left kidney. First, chemotherapy was performed, with no effects. Therefore, nephrectomy was subsequently performed. Histologically, the tumor showed the features of MTSCC with sarcomatoid component. Metastasis of the tumor into the lymph node was also observed. Although adjuvant chemotherapy was performed after nephrectomy, metastasis to the lungs and bone and local recurrence was observed. The patient is still alive 2 years after nephrectomy with metastasis and recurrence of the tumor. High-grade MTSCC shows a relatively poor prognosis, specifically MTSCC with metastasis upon nephrectomy.

Relationship between VHL mutation status, tumor VEGF expression and plasma VEGF measurement in sporadic renal cell carcinoma

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 4602-4602 ◽  
Author(s):  
P. Fergelot ◽  
N. Rioux-Leclercq ◽  
S. Zerrouki ◽  
K. Bensalah ◽  
J. Patard
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Performance Status ◽  
Enzyme Linked Immunosorbent Assay ◽  
Low Grade ◽  
Vegf Expression ◽  
Fuhrman Grade ◽  
Mutation Status ◽  
Mutational Status

4602 Background: The relationship between VHL mutation status and prognosis in renal cell carcinoma (RCC) remains controversial. The aim of this study was to evaluate prospectively the association between VHL status, tumor VEGF expression, plasma VEGF levels and usual prognostic parameters in RCC. Methods: 70 patients with clear cell RCCs were included in this study. Genomic DNA was extracted using the QIAmp DNA mini kit (Qiagen) from frozen tumor samples. Four amplimers covering the whole coding sequence and exon/intron junctions of the VHL gene were synthetized by PCR followed by Big Dye sequencing (Applied Biosystems). Mutation bearing sequences were confirmed in a second round of PCR and sequencing reactions. Tumor VEGF expression was determined by immunohistochemistry and plasma VEGF was measured by enzyme-linked immunosorbent assay (Quantikine immunoassay, R&D systems). Results were expressed in pg/ml. Qualitative and quantitative variables were compared by using Chi-square (Fischer exact test) and Student t tests, respectively. Results: A VHL mutation was found in 46 cases (65.7%). VHL mutations were localized in exon 1, 2 and 3 in 23, 16 and 7 cases respectively. Median tumor VEGF expression was 45% (5–100). Median plasma VEGF was 104 pg/ml (13–1430). A significant association was found between VHL mutation and N stage (p: 0.01), Fuhrman grade, symptoms at presentation (p: 0.02) or tumor size (p: 0.007). A VHL mutation was found in 83.5% of low grade (G1–2) and 80% of incidental tumors respectively. A trend towards more frequent VHL mutations was observed in T1 tumors (87% mutation rate, p: 0.07) and in good performance status patients. Interestingly, VEGF tumor expression and plasma VEGF levels were not significantly different among patients with tumors having or not mutated VHL (p: 0.7). Conclusion: VHL mutations are more frequent in small incidental low stage or low grade tumors. Although VHL inactivation was not specifically determined in this study, we failed to show any association between VHL mutational status and VEGF tumor or plasma expression suggesting that other pathways than the VHL/HIF axis are required for explaining the angiogenic phenotype of RCC. No significant financial relationships to disclose.

Differentiation of low grade from high grade clear cell renal cell carcinoma neoplasms using a CAD algorithm on four-phase CT.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. 4564-4564 ◽  
Author(s):  
Heidi Coy ◽  
Michael Douek ◽  
Jonathan Young ◽  
Matthew S. Brown ◽  
Jonathan Goldin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Low Grade ◽  
High Grade ◽  
Cell Renal Cell Carcinoma

scholarly journals Subgroup-Independent Mapping of Renal Cell Carcinoma—Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries

2021 ◽  
Vol 11 ◽  
Author(s):  
André Marquardt ◽  
Antonio Giovanni Solimando ◽  
Alexander Kerscher ◽  
Max Bittrich ◽  
Charis Kalogirou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Mitochondrial Gene ◽  
External Validation ◽  
Gene Signature ◽  
Gene Signatures ◽  
Transcriptomic Data

Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

Identification and validation of an 8-gene expression signature for predicting high-Fuhrman grade renal cell carcinoma.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 526-526
Author(s):  
Fangning Wan ◽  
Yao Zhu ◽  
Chengtao Han ◽  
Qinghua Xu ◽  
Bo Dai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Renal Mass ◽  
Gene Expression Signature ◽  
High Grade ◽  
Fuhrman Grade ◽  
Expression Signature ◽  
Biopsy Specimens

526 Background: Clear cell renal cell carcinoma (ccRCC) is a malignancy with heterogeneous outcomes. Currently, renal mass biopsies are commonly employed to extract disease characteristics and aid prognosis. Although the pathological diagnosis of malignant disease is accurate in contemporary reports, the classification of Fuhrman grade using biopsy specimens is still far from promising. Our aim is to generate a signature biomarker to distinguish high grade ccRCC that could be readily applied to clinical biopsy samples. Methods: Using the Cancer Genome Atlas (TCGA) database, a gene expression signature was developed to distinguish high-grade (G3/4) from low-grade (G1/2) disease. The expression profile was further validated for performance and clinical usage in 283 frozen renal cancer samples and 127 ex-vivo renal mass biopsy samples, respectively. The area under curve (AUC) was used to quantify discriminative ability and was compared using the De-long test. Results: Using the development dataset, we identified a 24-gene signature for high-grade disease with an AUC of 0.884. After applied to the replication dataset, an eight-gene profile was defined and achieved an AUC of 0.823. The accuracy of 8-gene panel was maintained in the RMB samples (AUC = 0.821). Conclusions: Using a two-stage replication design, we validated an eight-gene expression signature for predicting high Fuhrman grade of ccRCC. This tool may help to reveal the characteristics of ccRCC biopsy specimens.

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