Nicola Panvini

Patients with Crohn’s disease (CD) are at increased risk of developing small bowel cancer, since chronic inflammation may trigger the histopathological sequence that begins from low-grade dysplasia of the intestinal epithelium and may eventually lead to malignant transformation. Owing to their location in a portion of the gastrointestinal tract which is not easily accessible to conventional endoscopic techniques, the detection of CD-related small bowel cancers is still a clinical challenge. The radiological features of CD-related small bowel adenocarcinoma (SBA) in patients with CD have been described in some previous studies, including its appearance in both CT and MRI examinations. Radiological signs of active or fibrostenotic CD may be intermixed with those suggesting the presence of CD-related SBA. In CT studies, the most relevant findings consistent with malignant transformation are the presence of a stricture with irregular asymmetric thickening of small bowel walls, loss of mural stratification, and moderate enhancement after intravenous administration of iodinated contrast media, in association with enlarged adjacent mesenteric lymph nodes. Many of the CD-related SBA features that can be observed on CT imaging are similar to those detectable by MRI. This latter modality provides the additional value of the functional characterization of small bowel strictures, thereby helping to distinguish between inflammatory, fibrotic, and malignant stenosis in the setting of active CD. Positron Emission Tomography (PET)/CT enables the metabolic assessment of enlarged mesenteric lymph nodes, and PET/MRI fusion imaging can incorporate morphological, functional and metabolic information into a single set of imaging data, thus overcoming the limitations of the separate assessment of each individual modality. Owing to the low incidence and prevalence of this long-term complication of CD, we believe that a detailed multimodality pictorial essay on this topic, also including the PET-CT and fusion imaging documentation of some cases, would be useful to the medical literature. Graphical abstract

Background: To evaluate the diagnostic performance of a Machine Learning (ML) algorithm based on Texture Analysis (TA) parameters in the prediction of Pathological Complete Response (pCR) to Neoadjuvant Chemoradiotherapy (nChRT) in Locally Advanced Rectal Cancer (LARC) patients. Methods: LARC patients were prospectively enrolled to undergo pre- and post-nChRT 3T MRI for initial loco-regional staging. TA was performed on axial T2-Weighted Images (T2-WI) to extract specific parameters, including skewness, kurtosis, entropy, and mean of positive pixels. For the assessment of TA parameter diagnostic performance, all patients underwent complete surgical resection, which served as a reference standard. ROC curve analysis was carried out to determine the discriminatory accuracy of each quantitative TA parameter to predict pCR. A ML-based decisional tree was implemented combining all TA parameters in order to improve diagnostic accuracy. Results: Forty patients were considered for final study population. Entropy, kurtosis and MPP showed statistically significant differences before and after nChRT in patients with pCR; in particular, when patients with Pathological Partial Response (pPR) and/or Pathological Non-Response (pNR) were considered, entropy and skewness showed significant differences before and after nChRT (all p < 0.05). In terms of absolute value changes, pre- and post-nChRT entropy, and kurtosis showed significant differences (0.31 ± 0.35, in pCR, −0.02 ± 1.28 in pPR/pNR, (p = 0.04); 1.87 ± 2.19, in pCR, −0.06 ± 3.78 in pPR/pNR (p = 0.0005); 107.91 ± 274.40, in pCR, −28.33 ± 202.91 in pPR/pNR, (p = 0.004), respectively). According to ROC curve analysis, pre-treatment kurtosis with an optimal cut-off value of ≤3.29 was defined as the best discriminative parameter, resulting in a sensitivity and specificity in predicting pCR of 81.5% and 61.5%, respectively. Conclusions: TA parameters extracted from T2-WI MRI images could play a key role as imaging biomarkers in the prediction of response to nChRT in LARC patients. ML algorithms can be used to efficiently combine all TA parameters in order to improve diagnostic accuracy.

Objective: To evaluate the accuracy and reproducibility of hiatal surface area (HSA) measurement on dedicated multidetector computed tomography (MDCT) acquisition, in patients, previously subjected to laparoscopic sleeve gastrectomy (LSG), and affected by gastroesophageal reflux disease (GERD). Intraoperative HSA measurement was considered the reference standard. Methods: Fifty-two candidates for laparoscopic hiatal hernia repair were prospectively included in the study. MDCT images were acquired during swallowing of oral iodinated contrast media and during strain. Measurements were performed by nine readers divided into three groups according to their experience. Results were compared with intraoperative measurements by means of Spearman correlation coefficient. Reproducibility was evaluated with intra- and interreader agreement by means of weighted Cohen’s kappa and intraclass correlation coefficient (ICC). Results: Significant differences between MDCT and intraoperative HSA measurements were observed for swallowing imaging for less experienced readers (p = 0.037, 0.025, 0.028 and 0.019). No other statistically significant differences were observed (p > 0.05). The correlation between HSA measured intraoperatively and on MDCT was higher for strain imaging compared to swallowing (r = 0.94—0.92 vs 0.94—0.89). The overall reproducibility of MDCT HSA measurement was excellent (ICC of 0.95; 95% CI 0,8993 to 0,9840) independently of reader’s experience Conclusion: HSA can be accurately measured on MDCT images. This method is reproducible and minimally influenced by reader experience. The preoperative measurement of HSA has potential advantages for surgeons in terms of correct approach to hiatal defects in obese patient.

Purpose To determine whether the quality of a low-volume reduced bowel preparation (LV-RBP) for CT Colonography (CTC) is noninferior to full-volume reduced bowel preparation (FV-RBP) regimen. Methods In this randomized controlled trial, consecutive participants referred for CTC were randomly assigned to receive LV-RBP (52.5 g of PMF104 in 500 mL of water) or FV-RBP (105 g of PMF104 in 1000 mL of water). Images were independently reviewed by five blinded readers who rated the quality of bowel preparation from 0 (best score) to 3 (worst score). The primary outcome was the noninferiority of LV-RBP to FV-RBP in the proportion of colonic segments scored 0 for cleansing quality, with noninferiority margin of 10%. Volume of residual fluids, colonic distension, lesions and polyps detection rates and patient tolerability were secondary outcomes. Results From March 2019 to January 2020, 110 participants (mean age 65 years ± 14 [standard deviation]; 74 women) were allocated to LV-RBP (n = 55) or FV-RBP (n = 55) arms. There were 92% segment scored 0 in colon cleansing quality in LV-RBP and 94% in FV-RBP for prone scans, and 94% vs 92% for supine scans. Risk difference was – 2.1 (95% CI −5.9 to 1.7) and 1.5 (95% CI −2.4 to 5.4) for prone and supine positions, respectively. Residual fluids and colonic distension were also noninferior in LV-RBP. LV-RBP was associated with a lower number of evacuations during preparation (7 ± 5 vs 10 ± 6, p = 0.002). Conclusion The LV-RBP for CTC demonstrated noninferior quality of colon cleansing with improved gastrointestinal tolerability compared to FV-RBP regimen. Graphical abstract

Objective: The aim of this study was to perform a meta-analysis assessing the diagnostic yield of computed tomography (CT) for the identification of coronavirus disease 2019 (COVID-19) using repeated reverse transcriptase polymerase chain reaction testing or confirmed true-negative state as reference standard. Methods: In May 2020, we interrogated the MEDLINE, Embase, and CENTRAL databases. Pooled sensitivity, specificity, and diagnostic odds ratios of CT for COVID-19 identification were computed. Cumulative positive predictive value (PPV) and negative predictive value, stratified by disease prevalence, were calculated. Results: Ten articles were included (1332 patients). Pooled sensitivity, specificity, and summary diagnostic odds ratio of CT were 82% [95% confidence interval (CI), 79%–84%], 68% (95% CI, 65%–71%), and 18 (95% CI, 9.8–32.8). The PPV and negative predictive value were 54% (95% CI, 30%–77%) and 94% (95% CI, 88%–99%) at a COVID-19 prevalence lower than 40%, and 80% (95% CI, 62%–91%) and 77% (95% CI, 68%–85%) at a prevalence higher than 40%. Conclusion: CT yields higher specificity and PPV, albeit lower sensitivity, than previously reported for the identification of COVID-19.

Objective To conduct a multireader validation study to evaluate the interobserver variability and the diagnostic accuracy for the lung involvement by COVID-19 of COVID-19 Reporting and Data System (CO-RADS) score. Methods This retrospective study included consecutive symptomatic patients who underwent chest CT and reverse transcriptase-polymerase chain reaction (RT-PCR) from March 2020 to May 2020 for suspected COVID-19. Twelve readers with different levels of expertise independently scored each CT using the CO-RADS scheme for detecting pulmonary involvement by COVID-19. Receiver operating characteristic (ROC) curves were computed to investigate diagnostic yield. Fleiss’ kappa statistics was used to evaluate interreader agreement. Results A total of 572 patients (mean age, 63 ± 20 [standard deviation]; 329 men; 142 patients with COVID-19 and 430 patients without COVID-19) were evaluated. There was a moderate agreement for CO-RADS rating among all readers (Fleiss’ K = 0.43 [95% CI 0.42–0.44]) with a substantial agreement for CO-RADS 1 category (Fleiss’ K = 0.61 [95% CI 0.60–0.62]) and moderate agreement for CO-RADS 5 category (Fleiss’ K = 0.60 [95% CI 0.58–0.61]). ROC analysis showed the CO-RADS score ≥ 4 as the optimal threshold, with a cumulative area under the curve of 0.72 (95% CI 66–78%), sensitivity 61% (95% CI 52–69%), and specificity 81% (95% CI 77–84%). Conclusion CO-RADS showed high diagnostic accuracy and moderate interrater agreement across readers with different levels of expertise. Specificity is higher than previously thought and that could lead to reconsider the role of CT in this clinical setting. Key Points • COVID-19 Reporting and Data System (CO-RADS) demonstrated a good diagnostic accuracy for lung involvement by COVID-19 with an average AUC of 0.72 (95% CI 67–75%). • When a threshold of ≥ 4 was used, sensitivity and specificity were 61% (95% CI 52–69%) and 81% (95% CI 76–84%), respectively. • There was an overall moderate agreement for CO-RADS rating across readers with different levels of expertise (Fleiss’ K = 0.43 [95% CI 0.42–0.44]).

Layered enhancement at magnetic resonance enterography in inflammatory bowel disease: A meta-analysis

OBJECTIVE. The objective of our study was to perform a systematic review and meta-analysis to evaluate the diagnostic accuracy of dual-energy CT (DECT) for renal mass evaluation. MATERIALS AND METHODS. In March 2018, we searched MEDLINE, Cochrane Database of Systematic Reviews, Embase, and Web of Science databases. Analytic methods were based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Pooled estimates for sensitivity, specificity, and diagnostic odds ratios were calculated for DECT-based virtual monochromatic imaging (VMI) and iodine quantification techniques as well as for conventional attenuation measurements from renal mass CT protocols. I² was used to evaluate heterogeneity. The methodologic quality of the included studies and potential bias were assessed using items from the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS. Of the 1043 articles initially identified, 13 were selected for inclusion (969 patients, 1193 renal masses). Cumulative data of sensitivity, specificity, and summary diagnostic odds ratio for VMI were 87% (95% CI, 80–92%; I², 92.0%), 93% (95% CI, 90–96%; I², 18.0%), and 183.4 (95% CI, 30.7–1093.4; I², 61.6%), respectively. Cumulative data of sensitivity, specificity, and summary diagnostic odds ratio for iodine quantification were 99% (95% CI, 97–100%; I², 17.6%), 91% (95% CI, 89–94%; I², 84.2%), and 511.5 (95% CI, 217–1201; I², 0%). No significant differences in AUCs were found when comparing iodine quantification to conventional attenuation measurements (p = 0.79). CONCLUSION. DECT yields high accuracy for renal mass evaluation. Determination of iodine content with the iodine quantification technique shows diagnostic accuracy similar to conventional attenuation measurements from renal mass CT protocols. The iodine quantification technique may be used to characterize incidental renal masses when a dedicated renal mass protocol is not available.