Evaluation of the relationships between computed tomography features, pathological findings, and rrognostic risk assessment in gastrointestinal stromal tumors

Objectives The aim of this study was to correlate computed tomography (CT) findings with pathology in gastrointestinal stromal tumors (GISTs). Methods A retrospective evaluation of CT images of 44 patients with GISTs was performed. Computed tomography findings analyzed were location, size, margins, degree and pattern of contrast enhancement, angiogenesis, necrosis, signs of invasion, peritoneal effusion, peritoneal implants, surface ulceration, and calcifications. Associations between CT features and mitotic rate, Miettinen classes of risk, lesions size, and among CT features were investigated. χ 2 Test and Fisher test were performed. Results Mitotic rate was associated with margins (P = 0.016) and with adjacent organ invasion (P = 0.043). Pattern of contrast enhancement (P = 0.002), angiogenesis (P = 0.006), necrosis (P = 0.006), invasion of adjacent organs (P = 0.011), and margins (P = 0.006) were associated with classes of risk. Several associations (P < 0.05) between lesion size and CT features and among all the investigated CT features were found. Conclusions Computed tomography features could reflect GIST biology being associated with the mitotic rate and with classes of risk

Correlation between Primary Myelofibrosis and the Association of Portal Thrombosis with Portal-Biliary Cavernoma: US, MDCT, and MRI Features

Abstract Objective Myelofibrosis is a rare chronic myelolymphoproliferative disease and is associated with increased risk of venous thromboembolism. The objective of this study is to retrospectively evaluate patients with primary myelofibrosis who underwent abdominal US, MDCT and MRI, in order to identify the development of portal thrombosis and its correlation with portal-biliary cavernoma. Methods We evaluated 125 patients with initial diagnosis of primary myelofibrosis and nonspecific abdominal pain who had undergone US with color Doppler. In 13 patients (8 men, 5 females; age: 45–85), US detected portal thrombosis with associated portal-biliary cavernoma. All patients subsequently underwent contrast-enhanced MDCT and MRI and 4 patients MR-cholangiography. The correlation between primary myelofibrosis and portal thrombosis and cavernoma respectively was calculated using χ2 test. Results About 10% of patients with primary myelofibrosis preliminary evaluated with US had partial (8 pts) or complete (5 pts) portal thrombosis associated with portal-biliary cavernoma with a χ2 = 0. In all patients, US detected a concentric thickening of main bile duct (MBD) wall (mean value: 7 mm); color Doppler always showed dilated venous vessels within the thickened wall of the biliary tract. Contrast-enhanced CT and MRI confirmed thickening of MBD walls with their progressive enhancement and allowed better assessment of the extent of the portal system thrombosis. MR-cholangiography showed a thin appearance of the MBD lumen with evidence of ab extrinsic compression. Conclusions The evidence of portal thrombosis and portal-biliary cavernoma in 10% of the patients with primary myelofibrosis indicates a close correlation between the two diseases. In the detection of portal thrombosis and portal-biliary cavernoma, US with color Doppler is the most reliable and economical diagnostic technique while contrast-enhanced MDCT and MRI allow better assessment of the extent of the portal vein thrombosis and of the complications of myelofibrosis

Gastrointestinal stromal tumors: correlation between symptoms at presentation, tumor location and prognostic factors in 47 consecutive patients

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal stromal tumors (GIST) are mesenchymal tumors of the gastrointestinal tract, usually kit-positive, that are believed to originate from interstitial cell of Cajal, or their related stem cells. The most common clinical presentation of these tumors is gastrointestinal bleeding, otherwise they may cause intestinal obstruction, abdominal pain, a palpable mass, or can be incidentally detected during surgery or endoscopic/radiological procedures. Prognosis is related to the size of the tumor and to the mitotic rate; other prognostic factors are tumor location, tumor resection margins, tumor rupture, and c-kit mutation that may interfere with molecular target therapy efficacy.</p> <p>Aim</p> <p>Primary aim of this study was to report our experience regarding GIST patients, correlating symptoms at presentation with tumor localization and risk factors.</p> <p>Patients and methods</p> <p>47 consecutive patients undergone to surgical resection for GISTs were enrolled in a prospective study from December 1999 to March 2009. Patient's clinical and pathological features were collected and analysed.</p> <p>Results</p> <p>The most common symptom was abdominal pain. Bleeding in the digestive tract and abdominal pain were more frequent in gastric GISTs (58% and 61%); acute abdominal symptoms were more frequent in jejunal and ileal GISTs (40% and 60%), p < 0.05. We reported a mild correlation between the mitotic rate index and symptoms at presentation (p 0.074): this correlation was stronger if GISTs causing "acute abdominal symptoms" were compared with GISTs causing "abdominal pain" as main symptom (p 0.039) and with "incidental" GISTs (p 0.022).</p> <p>We observed an higher prevalence of symptomatic patients in the "high risk/malignant group" of both the Fletcher's and Miettines's classification (p < 0.05).</p> <p>Conclusion</p> <p>According with our findings symptoms correlate to tumor location, to class risk criteria as mitotic index and risk classifications, however we cannot conclude that symptoms are <it>per se </it>predictive of survival or patient's outcome.</p

Development and validation of artificial-intelligence-based radiomics model using computed tomography features for preoperative risk stratification of gastrointestinal stromal tumors

Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST's prognosis as determined by the Miettinen classification. Methods: patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. Results: 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. Conclusions: the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs

Radiomics analysis in gastrointestinal imaging: a narrative review

Background and Objective: To present an overview of radiomics radiological applications in major gastrointestinal oncological non-oncologic diseases, such as colorectal cancer, pancreatic cancer, gastro- oesophageal cancer, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and non-oncologic diseases, such as liver fibrosis, nonalcoholic steatohepatitis, and inflammatory bowel disease. Methods: A search of PubMed databases was performed for the terms “radiomic”, “radiomics”, “liver”, “small bowel”, “colon”, “GI tract”, and “gastrointestinal imaging” for English articles published between January 2013 and July 2022. A narrative review was undertaken to summarize literature pertaining to application of radiomics in major oncological and non-oncological gastrointestinal diseases. The strengths and limitation of radiomics, as well as advantages and major limitations and providing considerations for future development of radiomics were discussed. Key Content and Findings: Radiomics consists in extracting and analyzing a vast amount of quantitative features from medical datasets, Radiomics refers to the extraction and analysis of large amounts of quantitative features from medical images. The extraction of these data, integrated with clinical data, allows the construction of descriptive and predictive models that can build disease-specific radiomic signatures. Texture analysis has emerged as one of the most important biomarkers able to assess tumor heterogeneity and can provide microscopic image information that cannot be identified with the naked eye by radiologists. Conclusions: Radiomics and texture analysis are currently under active investigation in several institutions worldwide, this approach is being tested in a multitude of anatomical areas and diseases, with the final aim to exploit personalized medicine in diagnosis, treatment planning, and prediction of outcomes. Despite promising initial results, the implementation of radiomics is still hampered by some limitations related to the lack of standardization and validation of image acquisition protocols, feature segmentation, data extraction, processing, and analysi

Artificial intelligence based image quality enhancement in liver MRI. a quantitative and qualitative evaluation

Purpose To compare liver MRI with AIR Recon Deep Learning (TM)(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAiVE) sequences, in terms of quantitative and qualitative image analysis and scanning time. Material and methods This prospective study included fifty consecutive volunteers (31 female, mean age 55.5 +/- 20 years) from September to November 2021. 1.5 T MRI was performed and included three sets of images: axial single-shot fast spin-echo (SSFSE) T2 images, diffusion-weighted images(DWI) and apparent diffusion coefficient(ADC) maps acquired with both ARDL and NAiVE protocol; the NON-DL images, were also assessed. Two radiologists in consensus drew fixed regions of interest in liver parenchyma to calculate signal-to-noise-ratio (SNR) and contrast to-noise-ratio (CNR). Subjective image quality was assessed by two other radiologists independently with a five-point Likert scale. Acquisition time was recorded. Results SSFSE T2 objective analysis showed higher SNR and CNR for ARDL vs NAiVE, ARDL vs NON-DL(all P &lt; 0.013). Regarding DWI, no differences were found for SNR with ARDL vs NAiVE and, ARDL vs NON-DL (all P &gt; 0.2517).CNR was higher for ARDL vs NON-DL(P = 0.0170), whereas no differences were found between ARDL and NAiVE(P = 1). No differences were observed for all three comparisons, in terms of SNR and CNR, for ADC maps (all P &gt; 0.32). Qualitative analysis for all sequences showed better overall image quality for ARDL with lower truncation artifacts, higher sharpness and contrast (all P &lt; 0.0070) with excellent inter-rater agreement (k &gt;= 0.8143). Acquisition time was lower in ARDL sequences compared to NAiVE (SSFSE T2 = 19.08 +/- 2.5 s vs. 24.1 +/- 2 s and DWI = 207.3 +/- 54 s vs. 513.6 +/- 98.6 s, all P &lt; 0.0001). Conclusion ARDL applied on upper abdomen showed overall better image quality and reduced scanning time compared with NAiVE protocol

Multidisciplinary management of neuroendocrine neoplasia: a real-world experience from a referral center

Purpose: Multidisciplinary approach is widely advised for an effective care of patients with neuroendocrine neoplasia (NEN). Since data on efficacy of multidisciplinary management of NENs patients in referral centers are scanty, this study aimed at analyzing the modality of presentation and clinical outcome of patients with NENs managed by a dedicated multidisciplinary team. Methods. In this prospective observational study, we included all consecutive new patients visiting the Sant'Andrea Hospital in Rome (ENETS-Center of Excellence) between January 2014 and June 2018. Results. A total of 195 patients were evaluated. The most frequent sites were pancreas (38.5%), small bowel (22%), and lung (9.7%). Median Ki67 was 3%. After the first visit at the center, additional radiological and/or nuclear medicine procedures were requested in 163 patients (83.6%), whereas histological data revision was advised in 84 patients (43.1%) (revision of histological slides: 27.7%, new bioptic sampling: 15.4%). After that, disease imaging staging and grading was modified in 30.7% and 17.9% of patients, respectively. Overall, a change in therapeutic management was proposed in 98 patients (50.3%). Conclusions. Multidisciplinary approach in a dedicated team may lead to change of disease imaging staging and grading in a significant proportion of patients. Enhancing referral routes to dedicated-NEN center should be promoted, since it may improve patients' clinical outcome

Diagnostica per Immagini degli organi e apparati: Testa e Collo

Aggiornamento sulle tecniche di Imaging nello studio della Testa e del Collo con particolare riferimento alla TC-RM ed Ecografi