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

Abstract

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.

Small Renal Masses: Developing a Robust Radiomic Signature

“…In particular, our radiomic signature can better identify both benign and malignant lesions succeeding in the aim of decreasing the overtreatment and of better delineating a malignancy risk stratification and subsequent approach for malignant SRMs. Moreover, these data can be implemented with clinical, deep learning, radiometabolomics, SPECT and transcriptomics data [ 29 , 30 , 31 , 32 , 33 ] to improve performances. Klontzas et al [ 32 ] showed that the radiomics-only performance for distinguishing benign from malignant renal masses was 70%, while the integration of radiomics and metabolomics increased the performance in differentiating malignant lesions (solid, cystic or mixed) to at least 86%.…”

“…Klontzas et al [ 32 ] showed that the radiomics-only performance for distinguishing benign from malignant renal masses was 70%, while the integration of radiomics and metabolomics increased the performance in differentiating malignant lesions (solid, cystic or mixed) to at least 86%. Furthermore, Klontzas et al [ 30 ], by combining the 99m Tc Sestamibi uptake with radiomics in distinguishing benign oncocytic neoplasia, increased the diagnostic accuracy and improved positive and negative predictive value. Finally, transcriptomics and radiomics have been combined to assess the prognosis of RCC patients, as mentioned by Tang et al [ 29 ] (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively).…”

“…In particular, o radiomic signature can better identify both benign and malignant lesions succeeding the aim of decreasing the overtreatment and of better delineating a malignancy r stratification and subsequent approach for malignant SRMs. Moreover, these data can implemented with clinical, deep learning, radiometabolomics, SPECT a transcriptomics data [29][30][31][32][33] to improve performances. Klontzas et al [32] showed that t The tumor histotypes of the 17 patients belonging to the test set and the ones that are mislabelled are reported in Supplementary Materials (Table S1).…”