Renal Cell Carcinoma (RCC) is generally represented by low-FDG avidity, and [18F]FDG-PET/CT is not recommended to stage the primary tumor. However, its role to assess metastases is still unclear. The aim of this study was to evaluate the diagnostic accuracy of [18F]FDG-PET/CT to correctly identify RCC lung metastases using histology as standard of truth. Records of 350 patients affected by RCC and with CT evidence of at least one lung nodule, were retrospectively analyzed. Inclusion criteria were: a) histologically proven RCC; b) [18F]FDG-PET/CT performed prior to lung surgery; c) lung surgery with histological analysis of surgical specimens; d) complete follow-up available. A per-lesion analysis was performed, and diagnostic accuracy was reported as sensitivity and specificity, using histology as standard of truth. [18F]FDG-PET/CT semiquantitative parameters (Standardized Uptake Value [SUVmax], Metabolic Tumor Volume [MTV] and Total Lesion Glycolysis [TLG]) were collected for each lesion. Sixty-seven (n=67) patients with a total of 107 lesions were included: lung metastases from RCC were detected in 57/107 of cases, while 50/107 lesions were related to others lung malignancies. Applying a cut-off of SUVmax ≥2, the sensitivity and the specificity of [18F]FDG-PET/CT for detect RCC lung metastases were 33.3% (95% CI: 21.4% - 47.1%) and 26% (95%CI: 14.6% - 40.3%), respectively. The analysis demonstrated sub-optimal diagnostic accuracy of [18F]FDG-PET/CT to discriminate between RCC lung metastases versus other malignancies. However, semiquantitative analysis including also volumetric parameters (MTV and TLG) can support [18F]FDG-PET/CT image interpretation.

Background: We aimed to create a model of radiological and pathological criteria able to predict the upgrade rate of low-grade ductal carcinoma in situ (DCIS) to invasive carcinoma, in patients undergoing vacuum-assisted breast biopsy (VABB) and subsequent surgical excision. Methods: 3100 VABBs were retrospectively reviewed among which we reported 295 low-grade DCIS who subsequently underwent surgery. The association between patients’ features and the upgrade rate to invasive breast cancer (IBC) was evaluated by univariate analysis. Finally, we developed a predictive multivariable model based on the features which were significantly associated with the univariate analysis outcome. Results: the upgrade rate to invasive carcinoma was 10.8 %. At univariate analysis, the risk of upgrade was significantly lower in the absence of post- biopsy residual lesion (p<0.001), age > 50 (p=0.029), and in presence of low-grade DCIS only in specimens with microcalcifications (p=0.002). According to the final multivariable model, the predicted probability of diagnostic underestimation for a patient with all the three favourable features selected at univariate analysis was 1% (95% CI: 0.3%-4%). Conclusions: An easy to use predictive model of radiological and pathological criteria is able to identify patients with low-grade carcinoma in situ with low risk of upstaging to infiltrating carcinomas.