A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study

OBJECTIVES

To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC).

METHODS

Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index).

RESULTS

Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients.

CONCLUSIONS

The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.

Molecular imaging for non-invasive risk stratification of renal masses

Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99mTc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed.

The evolving management of small renal masses

Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.

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.

Imaging modalities for characterising T1 renal tumours: A systematic review and meta-analysis of diagnostic accuracy

Objectives

International guidelines recommend resection of suspected localised renal cell carcinoma (RCC), with surgical series showing benign pathology in 30%. Non-invasive diagnostic tests to differentiate benign from malignant tumours are an unmet need. Our objective was to determine diagnostic accuracy of imaging modalities for detecting cancer in T1 renal tumours.

Methods

A systematic review was performed for reports of diagnostic accuracy of any imaging test compared to a reference standard of histopathology for T1 renal masses, from inception until January 2023. Twenty-seven publications (including 2277 tumours in 2044 participants) were included in the systematic review, and nine in the meta-analysis.

Results

Forest plots of sensitivity and specificity were produced for CT (seven records, 1118 participants), contrast-enhanced ultrasound (seven records, 197 participants), [99mTc]Tc-sestamibi SPECT/CT (five records, 263 participants), MRI (three records, 220 participants), [18F]FDG PET (four records, 43 participants), [68Ga]Ga-PSMA-11 PET (one record, 27 participants) and [111In]In-girentuximab SPECT/CT (one record, eight participants). Meta-analysis returned summary estimates of sensitivity and specificity for [99mTc]Tc-sestamibi SPECT/CT of 88.6% (95% CI 82.7%-92.6%) and 77.0% (95% CI 63.0%-86.9%) and for [18F]FDG PET 53.5% (95% CI 1.6%-98.8%) and 62.5% (95% CI 14.0%-94.5%), respectively. A comparison hierarchical summary receiver operating characteristic (HSROC) model did not converge. Meta-analysis was not performed for other imaging due to different thresholds for test positivity.

Conclusion

The optimal imaging strategy for T1 renal masses is not clear. [99mTc]Tc-sestamibi SPECT/CT is an emerging tool, but further studies are required to inform its role in clinical practice. The field would benefit from standardisation of diagnostic thresholds for CT, MRI and contrast-enhanced ultrasound to facilitate future meta-analyses.

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation

Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Optimization (ASCO) blocks to capture anisotropic information in the images. Then, other newly designed blocks, i.e., Dense Dilated Enhancement Convolution (DDEC) blocks, are utilized to enhance feature propagation and reuse it across the network, thereby improving its segmentation accuracy. To allow the network to segment complex and small kidney tumors more effectively, the Atrous Spatial Pyramid Pooling (ASPP) module is incorporated in its middle layer. With its generalized pyramid feature, this module enables the network to better capture and understand context information at various scales within the images. In addition to this, the concurrent spatial and channel squeeze & excitation (scSE) attention mechanism is adopted to better comprehend and manage context information in the images. Additional encoding layers are also added to the base (U-Net) and connected to the original encoding layer through skip connections. The resultant enhanced U-Net structure allows for better extraction and merging of high-level and low-level features, further boosting the network's ability to restore segmentation details. In addition, the combined Binary Cross Entropy (BCE)-Dice loss is utilized as the network's loss function. Experiments, conducted on the KiTS19 dataset, demonstrate that the proposed ASD-Net network outperforms the existing segmentation networks according to all evaluation metrics used, except for recall in the case of kidney tumor segmentation, where it takes the second place after Attention-UNet.

Diagnostic Biopsy for Small Renal Tumours: A Survey of Current European Practice

Background and objective

Renal tumour biopsy (RTB) can help in risk stratification of renal tumours with implications for management, but its utilisation varies. Our objective was to report current practice patterns, experiences, and perceptions of RTB and research gaps regarding RTB for small renal masses (SRMs).

Methods

Two web-based surveys, one for health care providers (HCPs) and one for patients, were distributed via the European Association of Urology Young Academic Urologist Renal Cancer Working Group and the European Society of Residents in Urology in January 2023.

Key findings and limitations

The HCP survey received 210 responses (response rate 51%) and the patient survey 54 responses (response rate 59%). A minority of HCPs offer RTB to >50% of patients (14%), while 48% offer it in <10% of cases. Most HCPs reported that RTB influences (61.5%) or sometimes influences (37.1%) management decisions. Patients were more likely to favour active treatment if RTB showed high-grade cancer and less likely to favour active treatment for benign histology. HCPs identified situations in which they would not favour RTB, such as cystic tumours and challenging anatomic locations. RTB availability (67%) and concerns about delays to treatment (43%) were barriers to offering RTB. Priority research gaps include a trial demonstrating that RTB leads to better clinical outcomes, and better evidence that benign/indolent tumours do not require active treatment.

Conclusions and clinical implications

Utilisation of RTB for SRMs in Europe is low, even though both HCPs and patients reported that RTB results can affect disease management. Improving timely access to RTB and generating evidence on outcomes associated with RTB use are priorities for the kidney cancer community.

Patient summary

A biopsy of a kidney mass can help patients and doctors make decisions on treatment, but our survey found that many patients in Europe are not offered this option. Better access to biopsy services is needed, as well as more research on what happens to patients after biopsy.

Radiomics and Artificial Intelligence: Renal Cell Carcinoma

There is a clinical need for accurate diagnosis and prognostication of kidney cancer using imaging. Radiomics and deep learning methods applied to imaging have shown promise in tasks such as tumor segmentation, classification, staging, and grading, as well as assessment of preoperative scores and correlation with tumor biomarkers. Artificial intelligence is also expected to play a significant role in advancing personalized medicine for the treatment of renal cell carcinoma.

Radiomics and Artificial Intelligence in Renal Lesion Assessment

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma

OBJECTIVES

To investigate different radiomics models based on single phase and the different phase combinations of radiomics features from 3D tri-phasic CT to distinguish RO from chRCC.

METHODS

A total of 96 patients (30 RO and 66 chRCC) were enrolled in this study. Radiomics features were extracted from unenhanced phase (UP), corticomedullary phase (CMP), and nephrographic phase (NP) CT images. Feature selection was based on the least absolute shrinkage and selection operator regression (LASSO) method. The selected features were used to develop different radiomics models using logistic regression (LR) analysis, including model 1 (UP), model 2(CMP), model 3(NP), model 4(UP+CMP), model 5(UP+NP), model 6(CMP+NP), and model 7(UP+CMP+NP). The radiomics model demonstrating the highest discrimination performance was utilized to construct the combined model (model 8) with clinical factors. A nomogram based on the model 8 was established. To evaluate the diagnostic performance of the different models, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used. Delong's test was utilized to assess the statistical significance of the AUC improvement across the models.

RESULTS

Among the seven radiomics models, model 7 exhibited the highest AUC of 0.84 (95% CI 0.69, 0.99), and model 7 demonstrated a significantly superior AUC compared to the other radiomics models (all P < 0.05). The AUC values of radiomics models based on two phases (model4, mode5, mode6) were greater than the models based on single phase (model1, mode2, mode3) (all P < 0.05). Model 3 illustrated the best performance of the three radiomics models based on single phase with an AUC of 0.76 (95% CI 0.57, 099). Model 6 illustrated the best performance of the three radiomics models based on two-phases combination with an AUC of 0.83 (0.66, 0.99). Model 8 achieved an AUC of 0.93 (95% CI 0.83, 1.00) which is higher than those all radiomics models.

CONCLUSION

Radiomics models based on combination of radiomics features from UP, CMP, and NP can be a useful and promising technique to differentiate RO from chRCC. Moreover, the model combining clinical factors and radiomics features showed better classification performance to distinguish them.

Small Renal Masses: Developing a Robust Radiomic Signature

(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38-87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.

The Present and Future of Artificial Intelligence in Urological Cancer

Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.

Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

The increasing evidence of oncocytic renal tumors positive in ^99mTc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of ^99mTc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of ^99mTc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of ^99mTc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and ^99mTc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that ^99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with ^99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of ^99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of ^99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.

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.

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.

Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation

PURPOSE

To assess the performance of a machine learning model trained with contrast-enhanced CT-based radiomics features in distinguishing benign from malignant solid renal masses and to compare model performance with three abdominal radiologists.

METHODS

Patients who underwent intra-operative ultrasound during a partial nephrectomy were identified within our institutional database, and those who had pre-operative contrast-enhanced CT examinations were selected. The renal masses were segmented from the CT images and radiomics features were derived from the segmentations. The pathology of each mass was identified; masses were labeled as either benign [oncocytoma or angiomyolipoma (AML)] or malignant [clear cell, papillary, or chromophobe renal cell carcinoma (RCC)] depending on the pathology. The data were parsed into a 70/30 train/test split and a random forest machine learning model was developed to distinguish benign from malignant lesions. Three radiologists assessed the cohort of masses and labeled cases as benign or malignant.

RESULTS

148 masses were identified from the cohort, including 50 benign lesions (23 AMLs, 27 oncocytomas) and 98 malignant lesions (23 clear cell RCC, 44 papillary RCC, and 31 chromophobe RCCs). The machine learning algorithm yielded an overall accuracy of 0.82 for distinguishing benign from malignant lesions, with an area under the receiver operating curve of 0.80. In comparison, the three radiologists had significantly lower accuracies (p = 0.02) ranging from 0.67 to 0.75.

CONCLUSION

A machine learning model trained with CT-based radiomics features can provide superior accuracy for distinguishing benign from malignant solid renal masses compared to abdominal radiologists.

AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images

Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.