PURPOSE: Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS: Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS: From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the models selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION: Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Aims

Formal depiction of granulomatous inflammation associated with renal neoplasms has mainly consisted of case reports. Herein, we investigate the clinicopathological features and potential significance of granulomas associated with renal tumours from a large multi-institutional cohort.

Methods and results

One hundred and eleven study cases were collected from 22 institutions, including 57 partial nephrectomies and 54 radical nephrectomies. Patient ages ranged from 27 to 85 years (average = 60.1 years; male = 61%). Renal neoplasms included clear cell renal cell carcinoma (RCC; 86%), papillary RCC (8%), chromophobe RCC (3%), clear cell papillary RCC (1%), mixed epithelial stromal tumour (1%) and oncocytoma (1%). Granulomas were peritumoral in 36%, intratumoral in 24% and both in 40% of cases. Total granuloma count per case ranged from one to 300 (median = 15) with sizes ranging from 0.15 to 15 mm (mean = 1.9 mm). Necrotising granulomas were seen in 14% of cases. Histochemical stains for organisms were performed on 45% of cases (all negative). Sixteen cases (14%) had a prior biopsy/procedure performed, and eight patients had neoadjuvant immunotherapy or chemotherapy. Eleven patients (10%) had a confirmed diagnosis of sarcoidosis, including five in whom sarcoidosis was diagnosed after nephrectomy.

Conclusion

Based on this largest case-series to date, peri-/intratumoral granulomas associated with renal neoplasms may be more common than initially perceived. The extent of granulomatous inflammation can vary widely and may or may not have necrosis with possible aetiologies, including prior procedure or immunotherapy/chemotherapy. Although a clinical association with sarcoidosis is infrequent it can still occur, and the presence of granulomas warrants mention in pathology reports.