Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review.

OBJECTIVES: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim was to perform a systematic review of different methods of MRI intensity standardisation prior to radiomic feature extraction. METHODS: MEDLINE, EMBASE, and SCOPUS were searched for articles meeting the following eligibility criteria: MRI radiomic studies where one method of intensity normalisation was compared with another or no normalisation, and original research concerning patients diagnosed with diffuse gliomas. Using PRISMA criteria, data were extracted from short-listed studies including number of patients, MRI sequences, validation status, radiomics software, method of segmentation, and intensity standardisation. QUADAS-2 was used for quality appraisal. RESULTS: After duplicate removal, 741 results were returned from database and reference searches and, from these, 12 papers were eligible. Due to a lack of common pre-processing and different analyses, a narrative synthesis was sought. Three different intensity standardisation techniques have been studied: histogram matching (5/12), limiting or rescaling signal intensity (8/12), and deep learning (1/12)-only two papers compared different methods. From these studies, histogram matching produced the more reliable features compared to other methods of altering MRI signal intensity. CONCLUSION: Multiple methods of intensity standardisation have been described in the literature without clear consensus. Further research that directly compares different methods of intensity standardisation on glioma MRI datasets is required. KEY POINTS: • Intensity standardisation is a key pre-processing step in the development of robust radiomic signatures to evaluate diffuse glioma. • A minority of studies compared the impact of two or more methods. • Further research is required to directly compare multiple methods of MRI intensity standardisation on glioma datasets.

Proximal Gastrectomy versus Total Gastrectomy for Siewert Type II Adenocarcinoma of the Esophagogastric Junction: A Comprehensive Analysis of Data from the SEER Registry.

BACKGROUND: To determine the ideal surgical approach (total gastrectomy (TG) vs. proximal gastrectomy (PG)) for Siewert type II adenocarcinoma of the esophagogastric junction (AEG), we searched and analyzed the Surveillance, Epidemiology, and End Results (SEER) data. METHODS: Patients with Siewert type II AEG treated by TG or PG were identified from the 2004-2014 SEER dataset. We obtained the patients' overall survival (OS) and cancer-specific survival (CSS) and stratified the patients by surgical approach. We performed a propensity score 1 : 1 matching (PSM) analysis and a univariate and multivariate Cox proportional hazards model. RESULTS: A total of 2,217 patients with 6th AJCC stage IA-IIIB Siewert type II AEG was examined: 1,584 patients (71.4%) underwent PG, and 633 patients (28.6%) underwent TG. The follow-up time was 1-131 months. OS favored total gastrectomy before the PSM analysis (χ 2 = 3.952, p = 0.047), but after this analysis, there was no significant difference between TG and PG (χ 2 = 2.227, p = 0.136). The univariate and multivariate analyses identified age as an independent factor, and an X-tail analysis revealed 70 years as a cut-off point. The patients aged ≥ 70 years obtained a significant long-term OS benefit from PG compared to TG (χ 2 = 8.245, p = 0.004), and those aged < 70 years showed no difference between TG and PG (χ 2 = 0.167, p = 0.682). CONCLUSIONS: PG showed an equivalent survival benefit to TG in both the early and locally advanced stages of Siewert type II AEG. For elderly patients, PG is strongly recommended because of its clearer OS benefit compared to TG.