MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma

Navigating advanced renal cell carcinoma in the era of artificial intelligence

BACKGROUND

Research has helped to better understand renal cell carcinoma and enhance management of patients with locally advanced and metastatic disease. More recently, artificial intelligence has emerged as a powerful tool in cancer research, particularly in oncologic imaging. BODY: Despite promising results of artificial intelligence in renal cell carcinoma research, most investigations have focused on localized disease, while relatively fewer studies have targeted advanced and metastatic disease. This paper summarizes major artificial intelligence advances focusing mostly on their potential clinical value from initial staging and identification of high-risk features to predicting response to treatment in advanced renal cell carcinoma, while addressing major limitations in the development of some models and highlighting new avenues for future research.

CONCLUSION

Artificial intelligence-enabled models have a great potential in improving clinical practice in the diagnosis and management of advanced renal cell carcinoma, particularly when developed from both clinicopathologic and radiologic data.

The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma

OBJECTIVE

The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).

METHODS

A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software. Radiomic features were extracted via the FAE toolkit. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select features and build various machine learning models. Additionally, the largest cross-section of the tumor was cropped to train the deep learning model. Multiple deep learning models were trained to predict SDM in ccRCC patients. The results of the best machine learning model were then fused with those of the deep learning model to create a combined model.

RESULTS

Of the 944 radiomic features identified, 15 were closely associated with SDM. With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. Among the deep learning models, ResNet101 performed optimally, achieving AUC of 0.815 and 0.743 in the training and validation cohort, respectively. The combined model yielded an AUC of 0.863. Decision curve analysis suggested that the combined model offers superior clinical applicability.

CONCLUSION

The model integrates radiomics and deep learning, showing significant potential in predicting SDM in ccRCC patients. It holds promise for supporting clinical decision-making, reducing missed diagnoses of SDM, and guiding patients in further enhancing their systemic examinations.

Sub-regional Radiomics Analysis for Predicting Metastasis Risk in Clear Cell Renal Cell Carcinoma: A Multicenter Retrospective Study

RATIONALE AND OBJECTIVES

Clear cell renal cell carcinoma (ccRCC) is the most common malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic instances. Elucidating the composition of ccRCC holds promise for the discovery of highly sensitive biomarkers. Our objective was to utilize habitat imaging techniques and integrate multimodal data to precisely predict the risk of metastasis, ultimately enabling early intervention and enhancing patient survival rates.

MATERIAL AND METHODS

A retrospective analysis was performed on a cohort of 263 patients with ccRCC from three hospitals between April 2013 and March 2021. Preoperative CT images, ultrasound images, and clinical data were comprehensively analyzed. Patients from two campuses of Qilu Hospital of Shandong University were assigned to the training dataset, while the third hospital served as the independent testing dataset. A robust consensus clustering method was used to classify the primary tumor space into distinct sub-regions (i.e., habitats) using contrast-enhanced CT images. Radiomic features were extracted from these tumor sub-regions and subsequently reduced to identify meaningful features for constructing a predictive model for ccRCC metastasis risk assessment. In addition, the potential value of radiomics in predicting ccRCC metastasis risk was explored by integrating ultrasound image features and clinical data to construct and compare alternative models.

RESULTS

In this study, we performed k-means clustering within the tumor region to generate three distinct tumor subregions. We quantified the Hounsfiled Unit (HU) value, volume fraction, and distribution of high- and low-risk groups in each subregion. Our investigation focused on 252 patients with Habitat1 + Habitat3 to assess the discriminative power of these two subregions. We then developed a risk prediction model for ccRCC metastasis risk classification based on radiomic features extracted from CT and ultrasound images, and clinical data. The Combined model and the CT_Habitat3 model showed AUC values of 0.935 [95%CI: 0.902-0.968] and 0.934 [95%CI: 0.902-0.966], respectively, in the training dataset, while in the independent testing dataset, they achieved AUC values of 0.891 [95%CI: 0.794-0.988] and 0.903 [95%CI: 0.819-0.987], respectively.

CONCLUSION

We have identified a non-invasive imaging predictor and the proposed sub-regional radiomics model can accurately predict the risk of metastasis in ccRCC. This predictive tool has potential for clinical application to refine individualized treatment strategies for patients with ccRCC.

Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors.

PURPOSE

To identify high-risk subregions associated with synchronous distant metastasis.

METHODS

This study enrolled a total of 277 patients with ccRCC. Voxel intensity and local entropy values were compiled within the region of interest for all patients. Unsupervised k-means clustering yielded three subregions per tumor. Radiomic features were extracted, and random forest-based feature selection was conducted. The selected features were used in a multi-instance support vector machine (mi-SVM) model for training, and predictions were made on the validation cohort. Model performance was evaluated using five-fold cross-validation. The subregion with the highest score for patients with synchronous distant metastasis was identified across all cohorts.

RESULTS

The mi-SVM model yielded an average area under the curve (AUC) of 0.812 in the training cohort and 0.805 in the validation cohort. In the entire cohort of patients with synchronous distant metastasis, subregion 2, characterized by tumor periphery and intratumoral transitional components, accounted for the highest proportion (48.57%, 30.6/63) among all subregions. It represents a high-risk subregion for synchronous distant metastasis of clear cell renal cell carcinoma.

CONCLUSION

The peripheral and intratumoral transition zones of clear cell renal cell carcinoma are high-risk subregions associated with synchronous distant metastasis.

The Current Application and Future Potential of Artificial Intelligence in Renal Cancer

Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and outcome predictions are constantly evolving. We performed a narrative review utilizing PubMed electronic database to query AI as a method of analysis in kidney cancer research. Key search-words included: Artificial Intelligence, Supervised/Unsupervised Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, radiomics, pathomics, and kidney or renal neoplasms or cancer. 72 clinically relevant and impactful studies related to imaging, histopathology, and outcomes were recognized. We anticipate the incorporation of AI tools into future clinical decision-making for kidney cancer.

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.

The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients

BACKGROUND

The purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients.

METHODS

A total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1-weighted images (T1WI) and FS-T2WI (fat-suppressed T2-weighted images), group II consisted of patients with T1WI, FS-T2WI, and contrast enhanced MRI (CE-MRI), group III consisted of patients with T1WI, FS-T2WI, and T2-weighted images (T2WI), group IV consisted of patients with T1WI, FS-T2WI, CE-MRI, and T2WI, group V consisted of patients with T1WI, FS-T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS-T2WI, CE-MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group.

RESULTS

The machine learning model in group IV including T1WI, FS-T2WI, T2WI, and CE-MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE-MRI performed better than the models without CE-MRI(I vs. II, III vs. IV, V vs. VI).

CONCLUSIONS

The radiomics machine learning models based on CE-MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients.

AI-powered radiomics: revolutionizing detection of urologic malignancies

PURPOSE OF REVIEW

This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers.

RECENT FINDINGS

As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation.

SUMMARY

Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study

OBJECTIVES

To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC).

METHODS

A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA).

RESULTS

In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients.

CONCLUSIONS

The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients.

CLINICAL RELEVANCE STATEMENT

This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma.

KEY POINTS

• SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI

AIM

To explore the potential of the joint radiomics analysis of positron-emission tomography (PET) and magnetic resonance imaging (MRI) of primary tumours for predicting the risk of synchronous distant metastasis (SDM) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

18F-FDG PET and MRI images of PDAC patients from January 2011 to December 2020 were collected retrospectively. Patients (n=66) who received 18F-FDG PET/CT and MRI were included in a development group. Patients (n=25) scanned with hybrid PET/MRI were incorporated in an external test group. A radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm to select PET-MRI radiomics features of primary PDAC tumours. A radiomics nomogram was developed by combining the radiomics signature and important clinical indicators using univariate and multivariate analysis to assess patients' metastasis risk. The nomogram was verified with the employment of an external test group.

RESULTS

Regarding the development cohort, the radiomics nomogram was found to be better for predicting the risk of distant metastasis (area under the curve [AUC]: 0.93, sensitivity: 87%, specificity: 85%) than the clinical model (AUC: 0.70, p<0.001; sensitivity:70%, specificity: 65%) and the radiomics signature (AUC: 0.89, p>0.05; sensitivity: 65%, specificity:100%). Concerning the external test cohort, the radiomics nomogram yielded an AUC of 0.85.

CONCLUSION

PET-MRI based radiomics analysis exhibited effective prediction of the risk of SDM for preoperative PDAC patients and may offer complementary information and provide hints for cancer staging.

Adjuvant chemotherapy or no adjuvant chemotherapy? A prediction model for the risk stratification of recurrence or metastasis of nasopharyngeal carcinoma combining MRI radiomics with clinical factors

BACKGROUND

Dose adjuvant chemotherapy (AC) should be offered in nasopharyngeal carcinoma (NPC) patients? Different guidelines provided the different recommendations.

METHODS

In this retrospective study, a total of 140 patients were enrolled and followed for 3 years, with 24 clinical features being collected. The imaging features on the enhanced-MRI sequence were extracted by using PyRadiomics platform. The pearson correlation coefficient and the random forest was used to filter the features associated with recurrence or metastasis. A clinical-radiomics model (CRM) was constructed by the Cox multivariable analysis in training cohort, and was validated in validation cohort. All patients were divided into high- and low-risk groups through the median Rad-score of the model. The Kaplan-Meier survival curves were used to compare the 3-year recurrence or metastasis free rate (RMFR) of patients with or without AC in high- and low-groups.

RESULTS

In total, 960 imaging features were extracted. A CRM was constructed from nine features (seven imaging features and two clinical factors). In the training cohort, the area under curve (AUC) of CRM for 3-year RMFR was 0.872 (P <0.001), and the sensitivity and specificity were 0.935 and 0.672, respectively; In the validation cohort, the AUC was 0.864 (P <0.001), and the sensitivity and specificity were 1.00 and 0.75, respectively. Kaplan-Meier curve showed that the 3-year RMFR and 3-year cancer specific survival (CSS) rate in the high-risk group were significantly lower than those in the low-risk group (P <0.001). In the high-risk group, patients who received AC had greater 3-year RMFR than those who did not receive AC (78.6% vs. 48.1%) (p = 0.03).

CONCLUSION

Considering increasing RMFR, a prediction model for NPC based on two clinical factors and seven imaging features suggested the AC needs to be added to patients in the high-risk group and not in the low-risk group.

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

BACKGROUND

Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

METHODS

From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

RESULTS

After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

CONCLUSIONS

According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Development and external validation of a radiomics model for assessment of HER2 positivity in men and women presenting with gastric cancer

BACKGROUND

To develop and externally validate a conventional CT-based radiomics model for identifying HER2-positive status in gastric cancer (GC).

METHODS

950 GC patients who underwent pretreatment CT were retrospectively enrolled and assigned into a training cohort (n = 388, conventional CT), an internal validation cohort (n = 325, conventional CT) and an external validation cohort (n = 237, dual-energy CT, DECT). Radiomics features were extracted from venous phase images to construct the "Radscore". On the basis of univariate and multivariate analyses, a conventional CT-based radiomics model was built in the training cohort, combining significant clinical-laboratory characteristics and Radscore. The model was assessed and validated regarding its diagnostic effectiveness and clinical practicability using AUC and decision curve analysis, respectively.

RESULTS

Location, clinical TNM staging, CEA, CA199, and Radscore were independent predictors of HER2 status (all p < 0.05). Integrating these five indicators, the proposed model exerted a favorable diagnostic performance with AUCs of 0.732 (95%CI 0.683-0.781), 0.703 (95%CI 0.624-0.783), and 0.711 (95%CI 0.625-0.798) observed for the training, internal validation, and external validation cohorts, respectively. Meanwhile, the model would offer more net benefits than the default simple schemes and its performance was not affected by the age, gender, location, immunohistochemistry results, and type of tissue for confirmation (all p > 0.05).

CONCLUSIONS

The conventional CT-based radiomics model had a good diagnostic performance of HER2 positivity in GC and the potential to generalize to DECT, which is beneficial to simplify clinical workflow and help clinicians initially identify potential candidates who might benefit from HER2-targeted therapy.

Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

Background

Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.

Methods

This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.

Results

Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

Conclusions

The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.

Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

Background

The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation.

Purpose

To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC.

Material and Methods

In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values.

Results

Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively).

Conclusion

Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.

A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis

Background

To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7).

Methods

In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7.

Results

A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson’s correlation coefficient was statistically significant between Rad-score and CK7.

Conclusion

We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01099-0.

Artificial Intelligence in Kidney Cancer

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

A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

OBJECTIVES

This study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD).

METHODS

One hundred patients with ADPKD were randomly divided into training group (n = 69) and test group (n = 31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD.

RESULTS

Fourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group [training group: (AUC) = 0.7542, test group (AUC) = 0.7417]. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets [radiomics model (AUC) = 0.7505, radiomics nomogram model (AUC) = 0.8435, p value = 0.005]. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value.

CONCLUSION

radiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making. Trial registration ChiCTR, ChiCTR2100046739. Registered 27 May 2021-retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955.

Magnetic Resonance Imaging Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study

BACKGROUND

Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM.

PURPOSES

To develop and validate a 3 T MRI primary tumor radiomics-based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals.

FIELD STRENGTH AND SEQUENCES

A 3.0 T and fat-suppressed T1-weighted imaging.

ASSESSMENT

Quantitative imaging features were extracted from fat-suppressed T1-weighted (FS T1WI) images at the arterial phase.

STATISTICAL TESTS

Normally distributed data were compared by using t-tests, while the Mann-Whitney U test was used to evaluate non-normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant.

RESULTS

The AUCs of magnetic resonance imaging (MRI) Rad-score were 0.868 (95% confidence level [CI]: 0.613-0.852) and 0.772 (95% CI: 0.659-0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness.

DATA CONCLUSION

Pre-/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is the most common renal malignant tumor in elderly patients. The prognosis of renal cell carcinoma with distant metastasis is poor. We aim to construct a nomogram to predict the risk of distant metastasis in elderly patients with RCC to help doctors and patients with early intervention and improve the survival rate.

Methods

The clinicopathological information of patients was downloaded from SEER to identify all elderly patients with RCC over 65 years old from 2010 to 2018. Univariate and multivariate logistic regression analyzed the training cohort's independent risk factors for distant metastasis. A nomogram was established to predict the distant metastasis of elderly patients with RCC based on these risk factors. We used the consistency index (C-index), calibration curve, and area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical application value of the model.

Results

A total of 36,365 elderly patients with RCC were included in the study. They were randomly divided into the training cohort (N = 25,321) and the validation cohort (N = 11,044). In the training cohort, univariate and multivariate logistic regression analysis suggested that race, tumor histological type, histological grade, T stage, N stage, tumor size, surgery, radiotherapy, and chemotherapy were independent risk factors for distant metastasis elderly patients with RCC. A nomogram was constructed to predict the risk of distant metastasis in elderly patients with RCC. The training and validation cohort's C-indexes are 0.949 and 0.954, respectively, indicating that the nomogram has excellent accuracy. AUC of the training and validation cohorts indicated excellent predictive ability. DCA suggested that the nomogram had a better clinical application value than the traditional TN staging.

Conclusion

This study constructed a new nomogram to predict the risk of distant metastasis in elderly patients with RCC. The nomogram has excellent accuracy and reliability, which can help doctors and patients actively monitor and follow up patients to prevent distant metastasis of tumors.

OUP accepted manuscript

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.