Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status

Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images

OBJECTIVE

Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade.

METHODS

Between March 2013 and July 2023, a retrospective analysis was conducted on the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades Ⅲ and Ⅳ. This study included 201 patients from Hospital A who were divided into training set (n = 161) and an internal validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospital B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Subsequently, separate models for clinical factors, radiomics features, and their combinations were constructed. The model's performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA).

RESULTS

In total, 235 patients diagnosed with CCRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this study. A comparison of the predictive performances of different models revealed that the logistic regression model exhibited relatively good stability and robustness. The AUC of the combined model for the training, internal validation and external validation sets were 0.871, 0.785 and 0.826, respectively, which were higher than those of the clinical and imaging histology models. Furthermore, the calibration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0-0.93, the combined model demonstrated substantial clinical utility, as evidenced by DCA.

CONCLUSION

The application of US radiomics techniques enabled objective prediction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integrated use in clinical practice.

ADVANCES IN KNOWLEDGE

Previous studies have predominantly focused on using computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based on US images is more cost-effective, easily accessible and exhibits commendable performance. Consequently, this study offers a promising approach to maximizing the use of US examinations in future research.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Application of artificial intelligence in the diagnosis and treatment of urinary tumors

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.

The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis

BACKGROUND

Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT).

METHODS

The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC).

RESULTS

Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies.

CONCLUSIONS

To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study

PURPOSE

To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data.

MATERIALS AND METHODS

In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve.

RESULTS

A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model.

CONCLUSION

The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.

Applications of artificial intelligence in urologic oncology

PURPOSE

With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology.

MATERIALS AND METHODS

We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery."

RESULTS

A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.

CONCLUSIONS

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

BAG3 as a novel prognostic biomarker in kidney renal clear cell carcinoma correlating with immune infiltrates

PURPOSE

BCL-2-associated athanogene 3 (BAG3) is an anti-apoptotic protein that plays an essential role in the onset and progression of multiple cancer types. However, the clinical significance of BAG3 in kidney renal clear cell carcinoma (KIRC) remains unclear.

METHODS

Using Tumor IMmune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) database, we explored the expression, prognostic value, and clinical correlations of BAG3 in KIRC. In addition, immunohistochemistry (IHC) of HKH cohort further validated the expression of BAG3 in KIRC and its impact on prognosis. Gene Set Cancer Analysis (GSCA) was utilized to scrutinize the prognostic value of BAG3 methylation. Gene Ontology (GO) term analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to identify potential biological functions of BAG3 in KIRC. Single-sample gene set enrichment analysis (ssGSEA) was performed to confirm the correlation between BAG3 expression and immune cell infiltration.

RESULTS

BAG3 mRNA expression and protein expression were significantly downregulated in KIRC tissues compared to normal kidney tissues, associated with adverse clinical-pathological factors and poor clinical prognosis. Multivariate Cox regression analysis indicated that low expression of BAG3 was an independent prognostic factor in KIRC patients. GSEA analysis showed that BAG3 is mainly involved in DNA methylation and the immune-related pathways in KIRC. In addition, the expression of BAG3 is closely related to immune cell infiltration and immune cell marker set.

CONCLUSION

BAG3 might be a potential therapeutic target and valuable prognostic biomarker of KIRC and is closely related to immune cell infiltration.

Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading

BACKGROUND

Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI).

METHODS

This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation.

RESULTS

The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation.

CONCLUSIONS

The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.

Genetic and Epigenetic Characteristics in Isolated Pancreatic Metastases of Clear-Cell Renal Cell Carcinoma

Isolated pancreatic metastases of renal cell carcinoma (IsPMRCC) are a rare manifestation of metastatic, clear-cell renal cell carcinoma (RCC) in which distant metastases occur exclusively in the pancreas. In addition to the main symptom of the isolated occurrence of pancreatic metastases, the entity surprises with additional clinical peculiarities: (a) the unusually long interval of about 9 years between the primary RCC and the onset of pancreatic metastases; (b) multiple pancreatic metastases occurring in 36% of cases; (c) favourable treatment outcomes with a 75% 5-year survival rate; and (d) volume and growth-rate dependent risk factors generally accepted to be relevant for overall survival in metastatic surgery are insignificant in isPMRCC. The genetic and epigenetic causes of exclusive pancreatic involvement have not yet been investigated and are currently unknown. Conversely, according to the few available data in the literature, the following genetic and epigenetic peculiarities can already be identified as the cause of the protracted course: 1. high genetic stability of the tumour cell clones in both the primary tumour and the pancreatic metastases; 2. a low frequency of copy number variants associated with aggressiveness, such as 9p, 14q and 4q loss; 3. in the chromatin-modifying genes, a decreased rate of PAB1 (3%) and an increased rate of PBRM1 (77%) defects are seen, a profile associated with a favourable course; 4. an increased incidence of KDM5C mutations, which, in common with increased PBRM1 alterations, is also associated with a favourable outcome; and 5. angiogenetic biomarkers are increased in tumour tissue, while inflammatory biomarkers are decreased, which explains the good response to TKI therapy and lack of sensitivity to IT.

Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors

Background

KIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation.

Methods

Ninety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021-2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features.

Results

Three of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients' age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604-0.771), 0.829 (95 % CI: 0.768-0.890) and 0.874 (95 % CI: 0.822-0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796-0.964) and 0.827 (95%CI: 0.667-0.987), respectively.

Conclusion

The CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.

Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends

This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.

Artificial Intelligence in Urooncology: What We Have and What We Expect

INTRODUCTION

Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers.

METHODOLOGY

We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors.

RESULTS

Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors.

CONCLUSIONS

AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study

BACKGROUND

The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability.

METHODS

Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds.

RESULTS

Classification performance was significant (p < 0.05, H0:AUROC = 0.5) for 11 of 20 targets using either pipeline and for these targets the AUROCs were within ± 0.05 for the two pipelines, except for one target where the Proposed pipeline performance increased by > 0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage.

CONCLUSIONS

Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance.

TRIAL REGISTRATION

NCT03226886 (TRACERx Renal).

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Radiologist's Disease: Imaging for Renal Cancer

There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.

Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Exploration of 68Ga-labelled prostate-specific membrane antigen-11 PET/CT parameters for identifying PBRM1 status in primary clear cell renal cell carcinoma

AIM

To investigate the predictive value of ⁶⁸Ga-labelled prostate-specific membrane antigen-11 (⁶⁸Ga-PSMA-11) integrated positron-emission tomography (PET)/computed tomography (CT) in PBRM1-deficient clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

A total of 41 patients with ccRCC, were enrolled retrospectively and underwent ⁶⁸Ga-PSMA-11 PET/CT preoperatively. Radiological parameters, including CT attenuation value and maximum standard uptake value (SUVmax), were derived. Immunohistochemical and multiple immunofluorescences staining were performed to evaluate the PBRM1 status and immune response. The predictive value of imaging factors was analysed using a receiver operator characteristic curve analysis. Univariate and multivariate logistic regression analyses were used to investigate the relationship between clinical and radiological variables and PBRM1 status.

RESULTS

A total of 41 patients were included in this study, with 14 patients having PBRM1-deficient status. The tumour diameter on imaging and SUVmax differed significantly in patients with different PBRM1 expression statuses and no difference in CT attenuation was identified. Univariate and multivariate logistic regression analyses showed SUVmax was an obvious predictor for identification of PBRM1-deficient tumours. In addition, PBRM1-deficient tumours tended to be accompanied by greater cytotoxic T-cell infiltration, although most of them were in an exhausted state.

CONCLUSIONS

⁶⁸Ga-PSMA-11 PET/CT could be used to discriminate invasive PBRM1-deficient ccRCC.

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes' standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.

Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study

Purpose

Radiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients.

Methods and Materials

214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis.

Results

The best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated.

Conclusions

A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach

Background and purpose

Semantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas.

Methods

Between 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger’s sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM—Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups.

Results

Multislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup.

Conclusion

A machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.

The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model

We evaluated a multiclass classification model to predict estimated glomerular filtration rate (eGFR) groups in chronic kidney disease (CKD) patients using magnetic resonance imaging (MRI) texture analysis (TA). We identified 166 CKD patients who underwent MRI comprising Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images, apparent diffusion coefficient (ADC) maps, and T2* maps. The patients were divided into severe, moderate, and control groups based on eGFR borderlines of 30 and 60 mL/min/1.73 m2. After extracting 93 texture features (TFs), dimension reduction was performed using inter-observer reproducibility analysis and sequential feature selection (SFS) algorithm. Models were created using linear discriminant analysis (LDA); support vector machine (SVM) with linear, rbf, and sigmoid kernels; decision tree (DT); and random forest (RF) classifiers, with synthetic minority oversampling technique (SMOTE). Models underwent 100-time repeat nested cross-validation. Overall performances of our classification models were modest, and TA based on T1-weighted IP/OP/WO images provided better performance than those based on ADC and T2* maps. The most favorable result was observed in the T1-weighted WO image using RF classifier and the combination model was derived from all T1-weighted images using SVM classifier with rbf kernel. Among the selected TFs, total energy and energy had weak correlations with eGFR.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics

Objective:

Image-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR–Texture analysis.

Methods:

Thirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model.

Results:

A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively.

Conclusion:

Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs.

Advances in knowledge:

Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.

Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma

Objective

We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).

Methods

We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.

Results

After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.

Conclusion

We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.

NCAPG Promotes the Proliferation of Renal Clear Cell Carcinoma via Mediating with CDK1

Objective. Currently, lots of scholars have proved that the expression of NCAPG is associated with the prognosis of several cancers, while the relationship between NCAPG and renal clear cell carcinoma remains unclear, so the main aim of this research is to explore the effects of NCAPG on the progression of renal clear cell carcinoma. Methods. We observed the differential expression of NCAPG in several cancers from GEPIA online database, and the expression of NCAPG in renal clear cell carcinoma and normal tissue was compared and further verified by IHC assay. CCK-8 assay and clone formation experiment were conducted to observe the change of NCAPG on the proliferation. GraphPad was used for data analysis, and $t$ -test and ${\chi }^2$ analysis were used to analyze the correlation between NCAPG/CDK1 and renal clear cell carcinoma. Results. NCAPG was upregulated in renal clear cell carcinoma compared with the normal tissue, and the expression of NCAPG was associated with the clinical prognosis of pancreatic cancer especially with tumor size ( $P=0.010$ ). Knockdown NCAPG could restrain the proliferation of renal clear cell carcinoma. CDK1 was found to be tightly related with NCAPG, and the expression of CDK1 was also associated with the prognosis. Conclusions. NCAPG was upregulated in renal clear cell carcinoma, which was related with tumor size and overall survival. NCAPG might promote the proliferation of renal clear cell carcinoma via mediating CDK1. NCAPG/CDK1 complex might provide a new treatment strategy for lots of patients with renal clear cell carcinoma.

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management.

OBJECTIVE

To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization.

EVIDENCE ACQUISITION

The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr.

EVIDENCE SYNTHESIS

Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice.

CONCLUSIONS

A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis.

PATIENT SUMMARY

Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.

Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions

Radiogenomics is a field of translational radiology that aims to associate a disease's radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: "radiogenomics", "renal cell carcinoma", and "clear cell renal cell carcinoma". Articles included were limited to the English language and published between 2009-2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.

A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images

OBJECTIVES

We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma.

METHODS

Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated.

RESULTS

A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R² = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β =  - 0.75), and molecular subtype (β =  - 0.30).

CONCLUSIONS

The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis.

KEY POINTS

• The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.

The Next Paradigm Shift in the Management of Clear Cell Renal Cancer: Radiogenomics—Definition, Current Advances, and Future Directions

With improved molecular characterization of clear cell renal cancer and advances in texture analysis as well as machine learning, diagnostic radiology is primed to enter personalized medicine with radiogenomics: the identification of relationships between tumor image features and underlying genomic expression. By developing surrogate image biomarkers, clinicians can augment their ability to non-invasively characterize a tumor and predict clinically relevant outcomes (i.e., overall survival; metastasis-free survival; or complete/partial response to treatment). It is thus important for clinicians to have a basic understanding of this nascent field, which can be difficult due to the technical complexity of many of the studies. We conducted a review of the existing literature for radiogenomics in clear cell kidney cancer, including original full-text articles until September 2021. We provide a basic description of radiogenomics in diagnostic radiology; summarize existing literature on relationships between image features and gene expression patterns, either computationally or by radiologists; and propose future directions to facilitate integration of this field into the clinical setting.

Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model

Many significant efforts have so far been made to classify malignant tumors by using various machine learning methods. Most of the studies have considered a particular tumor genre categorized according to its originating organ. This has enriched the domain-specific knowledge of malignant tumor prediction, we are devoid of an efficient model that may predict the stages of tumors irrespective of their origin. Thus, there is ample opportunity to study if a heterogeneous collection of tumor images can be classified according to their respective stages. The present research work has prepared a heterogeneous tumor dataset comprising eight different datasets from The Cancer Imaging Archives and classified them according to their respective stages, as suggested by the American Joint Committee on Cancer. The proposed model has been used for classifying 717 subjects comprising different imaging modalities and varied Tumor-Node-Metastasis stages. A new non-sequential deep hybrid model ensemble has been developed by exploiting branched and re-injected layers, followed by bidirectional recurrent layers to classify tumor images. Results have been compared with standard sequential deep learning models and notable recent studies. The training and validation accuracy along with the ROC-AUC scores have been found satisfactory over the existing models. No model or method in the literature could ever classify such a diversified mix of tumor images with such high accuracy. The proposed model may help radiologists by acting as an auxiliary decision support system and speed up the tumor diagnosis process.

Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors

PURPOSE

To investigate the use of the combined model based on clinical and enhanced CT texture features for predicting the liver metastasis of high-risk gastrointestinal stromal tumors (GISTs).

METHODS

This retrospective study was conducted including 204 patients with pathologically confirmed high-risk GISTs from the Zhejiang Cancer Hospital from January 2015 to June 2021, and 76 cases of them were diagnosed with simultaneous liver metastasis. We randomly divided the cohort into a training cohort (n = 142) and a validation cohort (n = 62) with a ratio of 7:3. All volumes of interest (VOIs) of the high-risk GISTs were manually segmented on the portal venous phase CT images using the ITK-SNAP software. The least absolute shrinkage and selection operator (Lasso) algorithm was performed to determine the most valuable features from a total of 110 texture features extracted by the A-K software to reflect the texture information of the given VOIs. Texture-based predictive model was built from the selected texture features. Independent clinical risk factors were identified through univariate logistic analysis. Then, the texture-based model incorporated the clinical predictors to develop a combined model by multivariate logistic regression. Receiver operating characteristic curve, calibration curve, and decision curve analysis were utilized to analyze the discrimination capacity and clinical application value of the predictive models.

RESULTS

The nine optimal texture features were remained after the reduction of dimension using Lasso method. Another four clinical parameters (BMI, location, gastrointestinal bleeding, and CA125 level) were included in the clinical-based predictive model. Finally, with the combination of remaining texture and clinical features, a multivariate logistic regression classifier was built to predict the liver metastasis potential of high-risk GISTs. The remarkable classification performance of the combined model for the prediction of liver metastasis in the subjects with high-risk GISTs was obtained with area under curve (AUC) = 0.919, sensitivity = 83.9%, specificity = 89.7%, and accuracy = 84.9% in our validation group.

CONCLUSION

The texture-based radiomic signature derived from the portal venous phase CT images could predict liver metastasis of high-risk GISTs in a non-invasive way. Integrating additional clinical variables into the model further leads to an improvement of liver metastasis risk prediction.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma

PURPOSE

The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients.

METHODS

Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM.

RESULTS

The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup.

CONCLUSIONS

The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.

Radiomics to better characterize small renal masses

PURPOSE

Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies.

METHODS

A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness.

CONCLUSION

Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.

Decoding Genes: Current Update on Radiogenomics of Select Abdominal Malignancies

Technologic advances in chromosomal analysis and DNA sequencing have enabled genome-wide analysis of cancer cells, yielding considerable data on the genetic basis of malignancies. Evolving knowledge of tumor genetics and oncologic pathways has led to a better understanding of histopathologic features, tumor classification, tumor biologic characteristics, and imaging findings and discovery of targeted therapeutic agents. Radiogenomics is a rapidly evolving field of imaging research aimed at correlating imaging features with gene mutations and gene expression patterns, and it may provide surrogate imaging biomarkers that may supplant genetic tests and be used to predict treatment response and prognosis and guide personalized treatment options. Multidetector CT, multiparametric MRI, and PET with use of multiple radiotracers are some of the imaging techniques commonly used to assess radiogenomic associations. Select abdominal malignancies demonstrate characteristic imaging features that correspond to gene mutations. Recent advances have enabled us to understand the genetics of steatotic and nonsteatotic hepatocellular adenomas, a plethora of morphologic-molecular subtypes of hepatic malignancies, a variety of clear cell and non-clear cell renal cell carcinomas, a myriad of hereditary and sporadic exocrine and neuroendocrine tumors of the pancreas, and the development of targeted therapeutic agents for gastrointestinal stromal tumors based on characteristic KIT gene mutations. Mutations associated with aggressive phenotypes of these malignancies can sometimes be predicted on the basis of their imaging characteristics. Radiologists should be familiar with the genetics and pathogenesis of common cancers that have associated imaging biomarkers, which can help them be integral members of the cancer management team and guide clinicians and pathologists. Online supplemental material is available for this article. ^©RSNA, 2020 See discussion on this article by Luna (pp 1627-1630).

Kidney cancer management 3.0: can artificial intelligence make us better?

PURPOSE OF REVIEW

Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space.

RECENT FINDINGS

Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses.

SUMMARY

Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.

A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma

OBJECTIVES

We aimed to investigate the feasibility of predicting high-risk cytogenetic abnormalities (HRCAs) in patients with multiple myeloma (MM) using a spinal MRI-based radiomics method.

MATERIALS AND METHODS

In this retrospective study, we analyzed the radiomic features of 248 lesions (HRCA [n = 111] and non-HRCA [n = 137]) using T1WI, T2WI, and fat suppression T2WI. To construct the radiomics model, the top nine most frequent radiomic features were selected using logistic regression (LR) machine-learning processes. A combined LR model incorporating radiomic features and basic clinical characteristics (age and sex) was also built. Fivefold external cross-validation was performed, and a comparative analysis of 10 random fivefold cross-validation sets was used to verify result stability. Model performance was compared by plotting receiver operating characteristic curves and the area under the curve (AUC).

RESULTS

Comparable AUC values were observed between the radiomics model and the combined model in validation cohorts (AUC: 0.863 vs. 0.870, respectively, p = 0.206). The radiomics model had an AUC of 0.863, with a sensitivity of 0.789, a specificity of 0.787, a positive predictive value of 0.753, a negative predictive value of 0.824, and an accuracy of 0.788 in the validation cohort, which were comparable with the performance in the training cohorts.

CONCLUSIONS

Radiomic features of routine spinal MRI reflect differences between HRCAs and non-HRCAs in patients with MM. This MRI-based radiomics model might be a useful and independent tool to predict HRCAs in patients MM.

CT texture analysis of abdominal lesions – Part II: Tumors of the Kidney and Pancreas

It has been proven in a few early studies that radiomic analysis offers a promising opportunity to detect or differentiate between organ lesions based on their unique texture parameters. Recently, the utilization of CT texture analysis (CTTA) has been receiving significant attention, especially for response evaluation and prognostication of different oncological diagnoses. In this review article, we discuss the unique ability of radiomics and its subfield CTTA to diagnose lesions in the pancreas and kidney. We review studies in which CTTA was used for the classification of histology grades in pancreas and kidney tumors. We also review the role of radiogenomics in the prediction of the molecular and genetic subtypes of pancreatic tumors. Furthermore, we provide a short report on recent advancements of radiomic analysis in predicting prognosis and survival of patients with pancreatic and renal cancers.

Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer

To assess the capability of fractional water content (FWC) texture analysis (TA) to generate biologically relevant information from routine PET/MRI acquisitions for colorectal cancer (CRC) patients. Thirty consecutive primary CRC patients (mean age 63.9, range 42-83 years) prospectively underwent FDG-PET/MRI. FWC tumor parametric images generated from Dixon MR sequences underwent TA using commercially available research software (TexRAD). Data analysis comprised (1) identification of functional imaging correlates for texture features (TF) with low inter-observer variability (intraclass correlation coefficient: ICC > 0.75), (2) evaluation of prognostic performance for FWC-TF, and (3) correlation of prognostic imaging signatures with gene mutation (GM) profile. Of 32 FWC-TF with ICC > 0.75, 18 correlated with total lesion glycolysis (TLG, highest: rs = -0.547, p = 0.002). Using optimized cut-off values, five MR FWC-TF identified a good prognostic group with zero mortality (lowest: p = 0.017). For the most statistically significant prognostic marker, favorable prognosis was significantly associated with a higher number of GM per patient (medians: 7 vs. 1.5, p = 0.009). FWC-TA derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biological relevant information related to TLG, GM count, and provides prognostic information that can unlock new clinical applications for CRC patients.

Artificial Intelligence in Breast MRI Radiogenomics: Towards Accurate Prediction of Neoadjuvant Chemotherapy Responses

Neoadjuvant Chemotherapy (NAC) in breast cancer patients has considerable prognostic and treatment potential and can be tailored to individual patients as part of precision medicine protocols. This work reviews recent advances in artificial intelligence so as to enable the use of radiogenomics for accurate NAC analysis and prediction. The work addresses a new problem in radiogenomics mining: How to combine structural radiomics information and non-structural genomics information for accurate NAC prediction. This requires the automated extraction of parameters from structural breast radiomics data, and finding non-structural feature vectors with diagnostic value, which then are combined with genomics data acquired from exocrine bodies in blood samples from a cohort of cancer patients to enable accurate NAC prediction. A self-attention-based deep learning approach, along with an effective multi-channel tumour image reconstruction algorithm of high dimensionality, is proposed. The aim was to generate non-structural feature vectors for accurate prediction of the NAC responses by combining imaging datasets with exocrine body related genomics analysis.

Artificial intelligence in urological oncology: An update and future applications

There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.

Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability

Background: Multiple commercial and open-source software applications are available for texture analysis. Nonstandard techniques can cause undesirable variability that impedes result reproducibility and limits clinical utility. Objective: The purpose of this study is to measure agreement of texture metrics extracted by 6 software packages. Methods: This retrospective study included 40 renal cell carcinomas with contrast-enhanced CT from The Cancer Genome Atlas and Imaging Archive. Images were analyzed by 7 readers at 6 sites. Each reader used 1 of 6 software packages to extract commonly studied texture features. Inter and intra-reader agreement for segmentation was assessed with intra-class correlation coefficients. First-order (available in 6 packages) and second-order (available in 3 packages) texture features were compared between software pairs using Pearson correlation. Results: Inter- and intra-reader agreement was excellent (ICC 0.93-1). First-order feature correlations were strong (r>0.8, p<0.001) between 75% (21/28) of software pairs for mean and standard deviation, 48% (10/21) for entropy, 29% (8/28) for skewness, and 25% (7/28) for kurtosis. Of 15 second-order features, only co-occurrence matrix correlation, grey-level non-uniformity, and run-length non-uniformity showed strong correlation between software packages (0.90-1, p<0.001). Conclusion: Variability in first and second order texture features was common across software configurations and produced inconsistent results. Standardized algorithms and reporting methods are needed before texture data can be reliably used for clinical applications. Clinical Impact: It is important to be aware of variability related to texture software processing and configuration when reporting and comparing outputs.

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma

Objectives: To assess the feasibility of predicting molecular characteristics by computed tomography (CT) radiomics features, and predicting overall survival (OS) using combination of omics data in clear cell renal cell carcinoma (ccRCC).

Methods: Genetic data of 207 ccRCC patients was retrieved from The Cancer Genome Atlas (TCGA) and matched contrast-enhanced CT images were obtained from The Cancer Imaging Archive (TCIA). Another cohort of 175 ccRCC patients from West China Hospital was used as external validation. We first applied radiomics features and machine learning algorithms to predict genetic mutations and mRNA-based molecular subtypes. Next, we established predictive models for OS based on single omics, combined omics (radiomics+genomics, radiomics+transcriptomics, radiomics+proteomics) and all features (multi-omics).

Results: Using radiomics features, random forest algorithm showed good capacity to identify the mutations VHL (AUC=0.971), BAP1 (AUC=0.955), PBRM1 (AUC=0.972), SETD2 (AUC=0.949), and molecular subtypes m1 (AUC=0.973), m2 (AUC=0.968), m3 (AUC=0.961), m4 (AUC=0.953). The TCGA cohort was divided into training (n=104) and validation (n=103) sets. The radiomics model had promising prognostic value for OS in validation set (5-year AUC=0.775) and external validation set (5-year AUC=0.755). In the validation set, the radiomics+omics models enhanced predictive accuracy than single-omics models, and the multi-omics model made further improvement (5-year AUC=0.846). High-risk group of validation set predicted by multi-omics model showed significantly poorer OS (HR=6.20, 95%CI: 3.19-8.44, p<0.0001).

Conclusions: CT radiomics might be a feasible approach to predict genetic mutations, molecular subtypes and OS in ccRCC patients. Integrative analysis of radiogenomics may improve the survival prediction of ccRCC patients.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview

Imaging genomics refers to the establishment of the connection between invasive gene expression features and non-invasive imaging features. Tumor imaging genomics can not only understand the macroscopic phenotype of tumor, but also can deeply analyze the cellular and molecular characteristics of tumor tissue. In recent years, tumor imaging genomics has been a key in the field of medicine. The incidence of cancer in China has increased significantly, which is the main reason of disease death of urban residents. With the rapid development of imaging medicine, depending on imaging genomics, many experts have made remarkable achievements in tumor screening and diagnosis, prognosis evaluation, new treatment targets and understanding of tumor biological mechanism. This review analyzes the relationship between tumor radiology and gene expression, which provides a favorable direction for clinical staging, prognosis evaluation and accurate treatment of tumors.

Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study

OBJECTIVE

Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed.

MATERIAL AND METHODS

Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded.

RESULTS

The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively.

CONCLUSIONS

In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.