A review of 99mTc-sestamibi SPECT/CT for renal oncocytomas: A modified diagnostic algorithm

Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset

OBJECTIVES

To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset.

METHODS

A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN.

RESULTS

Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma.

CONCLUSION

Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors.

CRITICAL RELEVANCE STATEMENT

Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors.

KEY POINTS

• Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors.

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.

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.

Meta analysis of clinical prognosis of radiofrequency ablation versus partial nephrectomy in the treatment of early renal cell carcinoma

Objective

To systematically review the differences between radiofrequency ablation and partial nephrectomy in patients with early-stage renal cell carcinoma, and to provide evidence-based medical evidence for the choice of surgery for patients with early-stage renal cell carcinoma.

Methods

According to the search strategy recommended by the Cochrane Collaboration, Chinese databases such as CNKI, VIP Chinese Science and Technology Periodicals Database (VIP), and Wanfang Full-text Database were searched with Chinese search terms. And PubMed and MEDLINE as databases for English literature retrieval. Retrieve the relevant literature on renal cell carcinoma surgical methods published before May 2022, and further screen radiofrequency ablation and partial nephrectomy in patients with renal cell carcinoma The relevant literature on the application is analyzed. RevMan5.3 software was used for heterogeneity test and combined statistical analysis, sensitivity analysis, and subgroup analysis. Analysis, and draw forest plot, using Stata software Begger quantitative assessment of publication bias.

Results

A total of 11 articles were involved, including 2958 patients. According to the Jadad scale, 2 articles were of low quality, and the remaining 9 articles were of high quality. Results of this study demonstrates the advantages of radiofrequency ablation in early-stage renal cell carcinoma. The results of this meta-analysis showed that compared with partial nephrectomy, there was significant difference in the 5-year overall survival rate between radiofrequency ablation and partial nephrectomy and there was a statistically significant difference between the two surgical methods in the 5-year relapse free survival rate of early renal cell carcinoma.

Conclusion

1. Compared with partial nephrectomy, the 5-year relapse-free survival rate, the 5-year cancer specific survival rate and the overall 5-year survival rate were higher in the radiofrequency ablation group. 2. Compared with partial nephrectomy, there was no significant difference in the postoperative local tumor recurrence rate of radiofrequency ablation. 3. Compared with partial resection, radiofrequency ablation is more beneficial to patients with renal cell carcinoma.

99mTc-Sestamibi SPECT/CT and histopathological features of oncocytic renal neoplasia

BACKGROUND

^99mTc-Sestamibi Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) contributes to the non-invasive differentiation of renal oncocytoma (RO) from renal cell carcinoma (RCC) by characterising renal tumours as Sestamibi positive or Sestamibi negative regarding their ^99mTc-Sestamibi uptake compared to the non-tumoral renal parenchyma.

PURPOSE

To determine whether ^99mTc- Sestamibi uptake in renal tumour and the non-tumoral renal parenchyma measured using Standard Uptake Value (SUV) SPECT, has a beneficial role in differentiating RO from RCC.

MATERIAL AND METHODS

Fifty-seven renal tumours from 52 patients were evaluated. In addition to visual evaluation of ^99mTc-Sestamibi uptake, SUV_max measurements were performed in the renal tumour and the ipsilateral non-tumoral renal parenchyma. Analysis of the area under the receiver operating characteristic curve identified an optimal cut-off value for detecting RO, based on the relative ratio of ^99mTc- Sestamibi uptake.

RESULTS

Semiquantitative evaluation of ^99mTc-Sestamibi uptake did not improve the performance of ^99mTc- Sestamibi SPECT/CT in detecting RO. ^99mTc- Sestamibi SPECT/CT identifies a group of mostly indolent Sestamibi-positive tumours with low malignant potential containing RO, Low-Grade Oncocytic Tumours, Hybrid Oncocytic Tumours, and a subset of chromophobe RCCs.

CONCLUSION

The imaging limitations for accurate differentiation of Sestamibi-positive renal tumours mirror the recognised diagnostic complexities of the histopathologic evaluation of oncocytic neoplasia. Patients with Sestamibi-positive renal tumours could be better suited for biopsy and follow-up, according to the current active surveillance protocols.