Objectives To update the 2012 ESGAR consensus guidelines on the acquisition, interpretation and reporting of magnetic resonance imaging (MRI) for clinical staging and restaging of rectal cancer. Methods Fourteen abdominal imaging experts from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) participated in a consensus meeting, organised according to an adaptation of the RAND-UCLA Appropriateness Method. Two independent (non-voting) Chairs facilitated the meeting. 246 items were scored (comprising 229 items from the previous 2012 consensus and 17 additional items) and classified as ‘appropriate’ or ‘inappropriate’ (defined by ≥ 80 % consensus) or uncertain (defined by < 80 % consensus). Results Consensus was reached for 226 (92 %) of items. From these recommendations regarding hardware, patient preparation, imaging sequences and acquisition, criteria for MR imaging evaluation and reporting structure were constructed. The main additions to the 2012 consensus include recommendations regarding use of diffusion-weighted imaging, criteria for nodal staging and a recommended structured report template. Conclusions These updated expert consensus recommendations should be used as clinical guidelines for primary staging and restaging of rectal cancer using MRI. Key Points • These guidelines present recommendations for staging and reporting of rectal cancer. • The guidelines were constructed through consensus amongst 14 pelvic imaging experts. • Consensus was reached by the experts for 92 % of the 246 items discussed. • Practical guidelines for nodal staging are proposed. • A structured reporting template is presented.

The aim was to determine the incremental value of MRI compared with CT in the preoperative estimation of the peritoneal carcinomatosis index (PCI). CT and MRI examinations of patients with peritoneal carcinomatosis were evaluated. CT images were first analysed by two observers who determined a first PCI (PCI_CT). Then, the two observers reviewed MRI examinations in combination with CT and determined a second PCI (PCI_CT+MRI). The sensitivity and negative predictive value of the two imaging sets were determined using surgery as a reference standard (PCI_Ref). CT plus MRI was more accurate in predicting the surgical PCI than CT alone. The absolute difference between PCI_CT+MRI and PCI_Ref was lower than that between PCI_CT and PCI_Ref (mean(s.d.) 3·96(4·10) versus 4·89(4·73); P = 0·010). The number of true-positive findings increased from 106 to 125 for reader 1 and from 117 to 132 for reader 2 with the adjunct of MRI. For both readers, an increased sensitivity was obtained when both MRI and CT were used (from 63 to 81 per cent for reader 1; from 44 to 81 per cent for reader 2). The increase in sensitivity was greater for patients with a moderate volume of disease. The combination of CT and MRI improved the preoperative estimation of PCI compared with CT alone.

Tumor entropy derived from pretreatment texture analysis of contrast material–enhanced portal venous CT images may help predict survival in patients with advanced hepatocellular carcinoma treated with sorafenib. Purpose To determine whether texture features on pretreatment contrast material–enhanced computed tomographic (CT) images can help predict overall survival (OS) and time to progression (TTP) in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib. Materials and Methods This retrospective study included 92 patients with advanced HCC treated with sorafenib between January 2009 and April 2015 at two independent university hospitals. Sixty-four of the 92 patients (70%) (six women, 58 men; median age, 66 years) were included from institution 1 and constituted a training cohort; 28 patients (30%) (five women, 23 men; median age, 64 years) were included from institution 2 and constituted a validation cohort. Pretreatment CT texture analysis was performed on late arterial and portal venous phase HCC images. Mean gray-level intensity, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different...