As advances in kidney cancer are being made in all directions, from new technologies to ablate small renal masses to the development of several systemic therapies for the adjuvant and metastatic setting, there is a growing demand to improve imaging techniques for renal cell carcinoma (RCC). Currently, a lack of imaging discriminating between tumor types leads to overtreatment of renal masses, and imaging provides opaque insight into patients’ metastatic disease and response to therapy. Advancements in this field have focused on enhancing current imaging modalities and developing the fields of radiomics and radiogenomics, which infer insight into the molecular and genetic content of the tumor from radiographic features.

Conventional imaging techniques such as MRI, although routinely used for diagnosis, have also been shown to aid in assessing response to treatment. Arterial spin labeling as a surrogate for baseline tumor perfusion has been associated with improved response to treatment with tyrosine kinase inhibitors in metastatic RCC.¹ And although positron emission tomography–computed tomography (PET/CT) with the use of¹⁸ F-fluorodeoxy-glucose has had limited use in RCC given its physiologic uptake in the renal parenchyma, different radiotracers have been widely studied and used in different settings. Girentuximab (iodine-124-cG250), an antibody that selectively binds to carbonic anhydrase IX, which is overexpressed in Von Hippel–Lindau (VHL)–mutated pathways and expressed in 95% to 100% of clear cell RCC (ccRCC), is one of the most promising nuclear imaging methodologies. After the REDCET trial, which showed an advantage in using ¹²⁴I-girentuximab PET/CT (sensitivity and specificity of 86.2% and 85.9%, respectively) over contrast-enhanced CT (sensitivity and specificity of 75.5% and 46.8%, respectively) in detecting ccRCC from non-ccRCC in presurgical patients, different radiotracers targeting carbonic anhydrase IX have been studied.² The ZIRCON trial, a phase 3 study of ⁸⁹Zr-DFO-girentuximab PET/CT to detect ccRCC in indeterminate renal masses (cT1), published its results in the American Society of Clinical Oncology–Genitourinary 2023 meeting and reported a sensitivity and specificity to detect ccRCC of 86% and 87%, respectively.³ Limitations to both of these radiotracers are the logistics and timing of radiolabeled girentuximab administration, which is given 2 to 6 days prior to imaging, as well as costs and practicality.

In addition to adequately diagnosing ccRCC based on imaging, identifying chromophobe RCC and oncocytoma could also change practice in patients with small renal masses, as they have a more benign course.⁹⁹ Tc-sestamibi-SPECT (single-photon emission computerized tomography)/CT, a widely used nuclear imaging agent, accumulates in cells with high mitochondrial content and low multidrug resistance pump expression, which are characteristic of renal oncocytomas.^4,5 In a prospective study, 50 patients with T1 renal masses underwent imaging before surgery and imaging characteristics were compared to surgical specimens, resulting in a sensitivity of 87.5% and a specificity of 95.2% in differentiating oncocytomas and hybrid oncocytic/chromophobe tumors from other histologies.⁶ These results have also been validated in similar small-volume cohorts. Given the already widespread usability of ⁹⁹Tc-sestamibi SPECT/CT and the high concordance of imaging findings with pathology, results are promising in the identification of oncocytomas and other benign renal lesion; however, further larger studies are warranted. Additional radiotracers for PET/CT that have been studied in the setting of RCC include prostate-specific membrane antigen–PET, useful in detecting distant metastasis, and ¹¹C-acetate PET/CT, which integrates in cellular lipid structures and is highly expressed in ccRCC and papillary RCC.⁷ However, their clinical utility remains limited and additional studies are needed to validate their results in larger cohorts.

Even more exciting are the radiomics and radiogenomics of RCC, 2 closely related fields with promising developments in characterizing and predicting behavior and assessing treatment response in cancer. Radiomics uses predefined quantitative radiologic features (such as morphological characteristics, texture analysis, and intensity of different parameters within the tumor) and integrates this into algorithms and artificial intelligence models to predict malignancy, tumor histology, tumor grade, and molecular characteristics.⁴ Using a convolutional neural network, a type of deep learning algorithm that processes images using pixel data recognition, Xi et al developed a deep learning model that included clinical and radiologic MRI data of 1162 renal lesions and showed it to be superior to radiology experts, with an accuracy of 0.70 vs 0.60 (P = .053), sensitivity of 0.92 vs 0.80 (P = .017), and specificity of 0.41 vs 0.35 (P = .450).⁸

Radiogenomics, on the other hand, is the integration of radiomics with genetic tumoral data and molecular signatures, understanding that different genetic pathways have different phenotypic characteristics. In a cohort of 233 patients with ccRCC, contrast-enhanced CT findings were compared to genetic alterations and showed that VHL alterations were related to well-defined tumor margins, nodular tumor enhancement, and gross appearance of intratumoral vascularity. KDMC5 and BAP1 mutations were significantly associated with evidence of renal vein invasion, and PBRM1, together with VHL mutations, were significantly more common among solid ccRCC. BAP1, KDMC5, and SETD2 were absent in multicystic ccRCC.⁹

Radiogenomics has also been used in assessing oncological outcomes and response to treatment. One such example is the radiogenomic risk score (RRS), which was developed from a library of CT imaging features that were correlated to genetic signatures known to predict oncological outcomes. Patients were classified as RRS high vs low and followed prospectively. Patients with a high RRS had a median disease-specific survival of 40 months, compared to 120 months in patients with low RRS (P = .00024).¹⁰

Although radiomics and radiogenomics have shown promising utility in diagnosing renal masses, predicting clinical outcomes, and assessing their response to therapy, there is a lack of generalizability and clinical application. This might be due to the insufficiency of open access to the codes and images that are used for analysis.

Advances in kidney cancer go beyond novel therapies and technologies for treatment. Imaging techniques and technologies have also had an important advancement that are crucial to move the field forward and improve patient care. Further research and, mostly, validation of cohorts in larger populations are yet to happen for imaging-based biomarkers to become more readily available for RCC in a routine clinical setting.