Reliability of Functional and Diffusion MR Imaging Near Cerebral Cavernous Malformations.

BACKGROUND AND PURPOSE: Surgical resection of cerebral cavernous malformations close to eloquent regions frequently uses fMRI and DTI for surgical planning to best preserve neurologic function. This study investigates the reliability of fMRI and DTI near cerebral cavernous malformations. MATERIALS AND METHODS: Consecutive patients with cerebral cavernous malformations undergoing presurgical fMRI and DTI mapping were identified. Each cerebral cavernous malformation was hand-contoured; 2 sequential 4-mm expansion shells (S1 and S2) were created, generating 2 ROIs and 2 contralateral controls. Fractional anisotropy and regional homogeneity measurements were then extracted from each ROI and compared with the contralateral controls. Reliability, accuracy, and precision were compared as appropriate. RESULTS: Fifty-four patients were identified and included. Errors of fractional anisotropy were significantly lower than those of regional homogeneity in S1 and S2 (P < .001), suggesting that fractional anisotropy is more reliable than regional homogeneity near cerebral cavernous malformations. Proximity to cerebral cavernous malformations worsened the reliability of regional homogeneity (S1 versus S2, P < .001), but not fractional anisotropy (P = .24). While fractional anisotropy was not significantly biased in any ROI (P > .05), regional homogeneity was biased toward lower signals in S1 and S2 (P < .05), an effect that was attenuated with distance from cerebral cavernous malformations (P < .05). Fractional anisotropy measurements were also more precise than regional homogeneity in S1 and S2 (P < .001 for both). CONCLUSIONS: Our findings suggest that hemosiderin-rich lesions such as cerebral cavernous malformations may lead to artifactual depression of fMRI signals and that clinicians and surgeons should interpret fMRI studies near cerebral cavernous malformations with caution. While fMRI is considerably affected by cerebral cavernous malformation-related artifacts, DTI appears to be relatively unaffected and remains a reliable imaging technique near cerebral cavernous malformations.

Gallbladder polyps ultrasound: what the sonographer needs to know.

Gallbladder polyps are protuberances of the gallbladder wall projecting into the lumen. They are usually incidentally found during abdominal sonography or diagnosed on histopathology of a surgery specimen, with an estimated prevalence of up to 9.5% of patients. Gallbladder polyps are not mobile and do not demonstrate posterior acoustic shadowing; they may be sessile or pedunculated. Gallbladder polyps may be divided into pseudopolyps and true polyps. Pseudopolyps are benign and include cholesterolosis, cholesterinic polyps, inflammatory polyps, and localised adenomyomatosis. True gallbladder polyps can be benign or malignant. Benign polyps are most commonly adenomas, while malignant polyps are adenocarcinomas and metastases. There are also rare types of benign and malignant true gallbladder polyps, including mesenchymal tumours and lymphomas. Ultrasound is the first-choice imaging method for the diagnosis of gallbladder polyps, representing an indispensable tool for ensuring appropriate management. It enables limitation of secondary level investigations and avoidance of unnecessary cholecystectomies.

Reproducibility of rectal tumor volume delineation using diffusion-weighted MRI: Agreement on volumes between observers.

PURPOSE: To retrospectively evaluate the inter-observer agreement between a radiologist and a radiation oncologist and volume differences, in T2 and diffusion-weighted (DWI) MRI of gross tumor volume (GTV) delineation, in rectal cancer patients. MATERIALS AND METHODS: Two observers, a radiologist and a radiation oncologist, delineated GTVs of 50 patients on T2-weighted MRI (T2GTV) and echo planar DWI (DWIGTV). Observers agreement was assessed using DICE index, Bland-Altman analysis and intra-class correlation coefficient (ICC). Student's t-test was used for GTV comparison. RESULTS: Median T2GTV and DWIGTV were 17.09±14.12 cm3 (1.92-62.03) and 12.79±12.31 cm3 (1.23-62.25) for radiologist, and 16.82±13.66 cm3 (1.78-65.9) and 13.72±12.77 cm3 (1.29-69.75) for radiation oncologist. T2GTV were significantly larger compared to DWIGTV (P<0.001 and P<0.001, for both observers). Mean DICE index for T2GTV and DWIGTV were 0.80±0.07 and 0.77±0.06. The mean difference between the two observers were 0.26cm3 (95% CI: -5.36 to 5.88) and -1.13cm3 (95% CI: -5.70 to 3.44) for T2 and DWI volumes. The ICC for T2 volumes was 0.989 (95% CI: 0.981-0.994) (P<0.001) and 0.992 (95% CI: 0.986-0.996) (P<0.001) for DWI volumes. CONCLUSION: DWI resulted in smaller volumes delineation compared to T2-weighted MRI. Substantial and almost perfect agreements were reported for DWIGTV and T2GTV between radiologist and radiation oncologist. Due to the fact that DWI could be considered a simple technique for volume delineation for radiation oncologist, DWI could be used to improve quality in radiation planning for an accurate boost volume delineation when a dose escalation is investigated.

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.

INTRODUCTION: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response. PATIENTS AND METHODS: In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients. RESULTS: The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy. CONCLUSIONS: These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.