Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients

Wilms' Tumor: A Review of Clinical Characteristics, Treatment Advances, and Research Opportunities

Nephroblastoma is a complex childhood cancer with a generally favorable prognosis, well-defined incidence, and demographic profile but with significant challenges in terms of recurrence and long-term health outcomes. Although the management of this pathology has evolved, leading to improved survival rates, continued research into the long-term effects of treatment and the genetic factors influencing its development is still required. The survival landscape for Wilms tumor is evolving, with emerging research focusing on therapeutic biomarkers and genetic predispositions that influence treatment efficacy and survival rates. Identifying predictors for treatment response, such as specific genetic markers and histologic features, emerges as a critical area of study that could refine future interventions. The management of Wilms tumor is complex, taking into account the stage of the disease, histological classification, and individual patient factors, including age and the presence of syndromic associations. As treatment paradigms evolve, the integration of precision medicine approaches may enhance the ability of clinicians to personalize treatment to improve long-term survival outcomes for a broader range of patients. Recent advances in technology, including machine-learning approaches, have facilitated the identification of therapeutic biomarkers that correlate with clinical outcomes. This innovative method enhances the ability to integrate clinical and genetic data to predict disease trajectory and therapeutic response.

The application of machine learning based on computed tomography images in the identification of renal tumors in children

Background

The clinical manifestations of Wilms tumor and non-Wilms tumor in children are similar, and the only way to confirm the diagnosis is by postoperative pathology. Computed tomography (CT) is one of the main methods for preoperative diagnosis of the two, but it is also difficult to distinguish because it is easily affected by the subjective influence and the experience of the radiologists.

Methods

The CT images of 82 children with renal tumors admitted to the Department of Pediatric Urology, Shandong Provincial Hospital from January 2011 to March 2022 were retrospectively analyzed. First, we drew the two-dimensional (2D) region of interest (ROI) of the largest cross-section on the corticomedullary phase (CMP) and nephrogenic phase (NP) images, and extracted seven types of 107 features in the ROI. Then, the texture features with similarity greater than 95% and repetition less than 90% were screened out, and the remaining texture features were further screened by analysis of variance (ANOVA) and recursive feature elimination (RFE). Finally, 15 texture feature were used to build the machine learning (ML) models. We used the synthetic minority oversampling technique (SMOTE) and 10-fold cross-validation to build ML models and verified them in the training, testing, and internal validation sets. The area under the receiver-operating characteristic curve (AUC) and calibration curve were used to evaluate the diagnostic performance.

Results

We collected 77 CMP and 81 NP images, which were randomly divided into the training set and the testing set according to the ratio of 7:3. In the internal validation of CMP, the Mean-PCC-ANOVA-5-AE pipeline model achieved the highest AUC 0.792 [95% confidence interval (CI): 0.653-0.930], and its accuracy (ACC), sensitivity (SEN), and specificity (SPE) were 0.833, 0.539 and 0.927, respectively. Correspondingly, in NP, the Mean-PCC-ANOVA-2-LR pipeline model achieved the highest AUC 0.655 (95% CI: 0.485-0.82) in the internal validation. The ACC, SEN, and SPE were 0.696, 0.539, and 0.744, respectively.

Conclusions

The ML models based on CT images have good diagnostic efficiency in differentiating Wilms tumors from non-Wilms tumors in children.

A review on optimization of Wilms tumour management using radiomics

Background

Wilms tumour, a common paediatric cancer, is difficult to treat in low- and middle-income countries due to limited access to imaging. Artificial intelligence (AI) has been introduced for staging, detecting, and classifying tumours, aiding physicians in decision-making. However, challenges include algorithm accuracy, translation into conventional diagnosis, reproducibility, and reliability. As AI technology advances, radiomics, an AI tool, emerges to extract tumour morphology and stage information.

Objectives

This review explores the application of radiomics in Wilms tumour management, including its potential in diagnosis, prognosis, and treatment. Additionally, it discusses the future prospects of AI in this field and potential directions for automation-aided Wilms tumour treatment.

Methods

The review analyses various research studies and articles on the use of radiomics in Wilms tumour management. This includes studies on automated deep learning-based classification, interobserver variability in histopathological analysis, and the application of AI in staging, detecting, and classifying Wilms tumours.

Results

The review finds that radiomics offers several promising applications in Wilms tumour management, including improved diagnosis: it helps in classifying Wilms tumours from other paediatric kidney tumours, prognosis prediction: radiomic features can be used to predict both staging and response to preoperative chemotherapy, Treatment response assessment: Radiomics can be used to monitor the response of Wilms and to predict the feasibility of nephron-sparing surgery.

Conclusions

This review concludes that radiomics has the potential to significantly improve the diagnosis, prognosis, and treatment of Wilms tumours. Despite some challenges, such as the need for further research and validation, AI integration in Wilms tumour management offers promising opportunities for improved patient care.

Advances in knowledge

This review provides a comprehensive overview of the potential applications of radiomics in Wilms tumour management and highlights the significant role AI can play in improving patient outcomes. It contributes to the growing body of knowledge on AI-assisted diagnosis and treatment of paediatric cancers.