Long-Term Survival after Percutaneous Radiofrequency Ablation of Pathologically Proven Renal Cell Carcinoma in 100 Patients.

PURPOSE: To determine the long-term survival of patients treated with percutaneous radiofrequency (RF) ablation for pathologically proven renal cell carcinoma (RCC). MATERIALS AND METHODS: In this single-center retrospective study, 100 patients with 125 RCCs (100 clear-cell, 19 papillary, and 6 chromophobe) 0.8-8 cm in size treated with RF ablation were evaluated at a single large tertiary-care center between 2004 and 2015. Technical success, primary and secondary technique efficacy, and pre- and postprocedural estimated glomerular filtration rate (eGFR) at 3-6 months and 2-3 years were recorded. Overall survival, cancer-specific survival, and local tumor progression-free survival were calculated by Kaplan-Meier survival curves. Complications were classified per the Clavien-Dindo system. Statistical testing was done via χ2 tests for proportions and paired t test for changes in eGFR. Statistical significance was set at α = 0.05. RESULTS: Overall technical success rate was 100%, and primary and secondary technique efficacy rates were 90% and 100%, respectively. Median follow-up was 62.8 months, ranging from 1 to 120 months. The 10-year overall, cancer-specific, and local progression-free survival rates were 32%, 86%, and 92%, respectively. The number of ablation probes used was predictive of residual unablated tumor (P < .001). There were no significant changes in preprocedure vs 2-3-years postprocedure eGFR (65.2 vs 62.1 mL/min/1.73 m2; P = .443). There was a 9% overall incidence of complications, the majority of which were grade I. CONCLUSIONS: Image-guided percutaneous RF ablation of RCCs is effective at achieving local control and preventing cancer-specific death within 10 years from initial treatment.

Description and assessment of a registration-based approach to include bones for attenuation correction of whole-body PET/MRI.

PURPOSE: Attenuation correction for whole-body PET/MRI is challenging. Most commercial systems compute the attenuation map from MRI using a four-tissue segmentation approach. Bones, the most electron-dense tissue, are neglected because they are difficult to segment. In this work, the authors build on this segmentation approach by adding bones using a registration technique and assessing its performance on human PET images. METHODS: Twelve oncology patients were imaged with FDG PET/CT and MRI using a Turbo-FLASH pulse sequence. A database of 121 attenuation correction quality CT scans was also collected. Each patient MRI was compared to the CT database via weighted heuristic measures to find the "most similar" CT in terms of body geometry. The similar CT was aligned to the MRI with a deformable registration method. Two MRI-based attenuation maps were computed. One was a standard four-tissue segmentation (air, lung, fat, and lean tissue) using basic image processing techniques. The other was identical, except the bones from the aligned CT were added. The PET data were reconstructed with the patient's CT-based attenuation map (the silver standard) and both MRI-based attenuation maps. The relative errors of the MRI-based attenuation corrections were computed in 14 standardized volumes of interest, in lesions, and over whole tissues. The squared Pearson correlation coefficient was also calculated over whole tissues. Statistical testing was done with ANOVAs and paired t-tests. RESULTS: The MRI-based attenuation correction ignoring bone had relative errors ranging from -37% to -8% in volumes of interest containing bone. By including bone, the magnitude of the relative error was reduced in all cases (p<0.001), ranging from -3% to 4%. Further, the relative error in volumes of interest adjacent to bone was improved from a mean of -7.5% to 2% (p<0.001). In the other seven volumes of interest, including bone reduced the magnitude of relative error in three cases (p<0.001), had no effect in three cases, and increased relative error in one case. There was no statistically significant difference in the relative error in lesions. Over whole tissues, including bone slightly increased relative error in lung from 7.7% to 8.0% (p=0.002), in fat from 8.5% to 9.2% (p<0.001), and in lean tissue from -2.1% to 2.6% (p<0.001), but reduced the magnitude of relative error in bone from -14.6% to 1.3% (p<0.001). The correlation coefficient was essentially unchanged in all tissues regardless of whether bone was included or not. CONCLUSIONS: The approach to include bones in MRI-based attenuation maps described in this work improves quantification of whole-body PET images in and around bony anatomy. The reduction in error is often large (tens of percents), and could alter image interpretation and subsequent patient care. Changes in other parts of the PET image are minimal and likely not of clinical significance.

Variable lung density consideration in attenuation correction of whole-body PET/MRI.

UNLABELLED: Present attenuation-correction algorithms in whole-body PET/MRI do not consider variations in lung density, either within or between patients; this may adversely affect accurate quantification. In this work, a technique to incorporate patient-specific lung density information into MRI-based attenuation maps is developed and compared with an approach that assumes uniform lung density. METHODS: Five beagles were scanned with (18)F-FDG PET/CT and MRI. The relationship between MRI and CT signal in the lungs was established, allowing the prediction of attenuation coefficients from MRI. MR images were segmented into air, lung, and soft tissue and converted into attenuation maps, some with constant lung density and some with patient-specific lung densities. The resulting PET images were compared by both global metrics of quantitative fidelity (accuracy, precision, and root mean squared error) and locally with relative error in volumes of interest. RESULTS: A linear relationship was established between MRI and CT signal in the lungs. Constant lung density attenuation maps did not perform as well as patient-specific lung density attenuation maps, regardless of what constant density was chosen. In particular, when attenuation maps with patient-specific lung density were used, precision, accuracy, and root mean square error improved in lung tissue. In volumes of interest placed in the lungs, relative error was significantly reduced from a minimum of 12% to less than 5%. The benefit extended to tissues adjacent to the lungs but became less important as distance from the lungs increased. CONCLUSION: A means of using MRI to infer patient-specific attenuation coefficients in the lungs was developed and applied to augment whole-body MRI-based attenuation maps. This technique has been shown to improve the quantitative fidelity of PET images in the lungs and nearby tissues, compared with an approach that assumes uniform lung density.