Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma

Evaluation of prognostic factors for late recurrence in clear cell renal carcinoma: an institutional study

Background and objectives

Following nephrectomy with curative intent, a subset of patients diagnosed with non-metastatic renal cell carcinoma (nmRCC) will present late recurrences, with metastatic relapses after 5 years from the surgical intervention. The aim of this study is to evaluate the prevalence of late recurrences in Romanian patients with nmRCC that have undergone surgery and to assess the clinicopathological characteristics prognostic for late-relapse RCC.

Materials and methods

This is a single-center, retrospective and observational study that analyzed patients with nmRCC with clear cell histology who underwent surgical resection of the primary tumor with curative intent. The patients included in the study were treated and further surveilled according to a personalized follow-up plan between January 2011 and December 2012 in The Oncology Institute "Prof. Dr. Ion Chiricuţă", Cluj-Napoca, Romania. Study endpoints included median disease-free survival (DFS), median overall survival (OS), as well as evaluation of possible prognostic factors indicative of late relapse.

Results

In the study cohort (n=51), the median DFS was 46 months and median OS was 130 months. DFS was significantly correlated with the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) score (p=0.04, HR=2.48; 95% CI [1.02, 6.01]), neutrophil to lymphocyte ratio (NLR) (a higher NLR value was associated with a poorer DFS, p=0.035), tumor size (T4 tumors vs. T1 p<0.05, HR=9,81; 95% CI [2.65, 36.27]) and Fuhrman nuclear grade (Fuhrman grade 1 vs. Fuhrman grade 3 p<0.05, HR=4,16; 95% CI = [1.13,15.22]). Fifty one percent of the patients included experienced disease relapse. From this subgroup, a significant percentage of 42% patients presented disease recurrence after 60 months from nephrectomy. OS was correlated to IMDC score (p=0.049, HR=2.36; 95% CI [1, 5.58]) and Fuhrman nuclear grade (Fuhrman grade 1 vs. Fuhrman grade 3 p<0.05, HR=3,97; 95% CI [1.08, 14.54]).

Conclusions

The results of this study support the previously presented biological behavior of RCC, demonstrating that late recurrences in RCC are not uncommon occurrences and patients with localized RCC should be followed up for a longer interval after the surgery for the primary tumor. In addition, the study strengthens the data supporting certain biomarkers as valuable prognostic factors determining survival outcomes of patients with RCC.

Prognostic models for predicting oncological outcomes after surgical resection of a nonmetastatic renal cancer: A critical review of current literature

Prognostic models can be valuable for clinicians in counseling and monitoring patients after the surgical resection of nonmetastatic renal cell carcinoma (nmRCC). Over the years, several risk prediction models have been developed, evolving significantly in their ability to predict recurrence and overall survival following surgery. This review comprehensively evaluates and critically appraises current prognostic models for nm-RCC after nephrectomy. The last 2 decades have witnessed a notable increase in the development of various prognostic risk models for RCC, incorporating clinical, pathological, genomic, and molecular factors, primarily using retrospective data. Only a limited number of these models have been developed using prospective data, and their performance has been less effective than expected when applied to broader, real-life patient populations. Recently, artificial intelligence (AI), especially machine learning and deep learning algorithms, has emerged as a significant tool in creating survival prediction models. However, their widespread application remains constrained due to limited external validation, a lack of cost-effectiveness analysis, and unconfirmed clinical utility. Although numerous models that integrate clinical, pathological, and molecular data have been proposed for nm-RCC risk stratification, none have conclusively demonstrated practical effectiveness. As a result, current guidelines do not endorse a specific model. The ongoing development and validation of AI algorithms in RCC risk prediction are crucial areas for future research.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Pachymic acid activates TP53INP2/TRAF6/caspase-8 pathway to promote apoptosis in renal cell carcinoma cells

While pachymic acid (PA), a key component of Poria cocos (Schw.), has demonstrated anti-tumor effects in lung, breast, and pancreatic cancers, its impact on renal cell carcinoma (RCC) is unclear. This study evaluated the effect of PA on proliferation, migration, and apoptosis in human renal cancer A498 and ACHN cells as well as in cancer xenograft mice using wound scratch test, Western blotting, and co-immunoprecipitation assays. In a dose- and time-dependent manner, PA exhibited significant inhibition of RCC cell proliferation, migration, and invasion, accompanied by the induction of apoptosis. Additionally, PA upregulated the expression of tumor protein p53-inducible nuclear protein 2 (TP53INP2) and tumor necrosis factor receptor-associated factor 6 (TRAF6), which were downregulated in renal papillary and chromophobe carcinoma, resulting in inhibited tumor growth in mice. PA treatment elevated cleaved-caspase 3 and 8, and PARP levels, and facilitated TP53INP2 and TRAF6 binding to caspase 8, promoting its ubiquitination. Molecular docking revealed interactions between PA and TP53INP2, TRAF6. In summary, PA inhibits RCC development by upregulating TP53INP2 and promoting TRAF6-induced caspase 8 ubiquitination, activating apoptotic pathways.

Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma

BACKGROUND

Patients with renal cell carcinoma (RCC) have an elevated risk of chronic kidney disease (CKD) following nephrectomy. Therefore, continuous monitoring and subsequent interventions are necessary. It is recommended to evaluate renal function postoperatively. Therefore, a tool to predict CKD onset is essential for postoperative follow-up and management.

METHODS

We constructed a cohort using data from eight tertiary hospitals from the Korean Renal Cell Carcinoma (KORCC) database. A dataset of 4389 patients with RCC was constructed for analysis from the collected data. Nine machine learning (ML) models were used to classify the occurrence and nonoccurrence of CKD after surgery. The final model was selected based on the area under the receiver operating characteristic (AUROC), and the importance of the variables constituting the model was confirmed using the shapley additive explanation (SHAP) value and Kaplan-Meier survival analyses.

RESULTS

The gradient boost algorithm was the most effective among the various ML models tested. The gradient boost model demonstrated superior performance with an AUROC of 0.826. The SHAP value confirmed that preoperative eGFR, albumin level, and tumor size had a significant impact on the occurrence of CKD after surgery.

CONCLUSIONS

We developed a model to predict CKD onset after surgery in patients with RCC. This predictive model is a quantitative approach to evaluate post-surgical CKD risk in patients with RCC, facilitating improved prognosis through personalized postoperative care.

Prediction of Wilms' Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System

Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms' tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms' tumors. A total of 63 patients (age range: 6 months-14 years) were included in this study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms' tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors' images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors' functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms' tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms' tumors.