Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward.

The present letter to the editor is related to the study with the title "Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models". Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians' tasks. The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.

Efficacy and safety of three-dimensional magnetically assisted capsule endoscopy for upper gastrointestinal and small bowel examination.

BACKGROUND: Magnetically assisted capsule endoscopy (MACE) showed the feasibility for upper gastrointestinal examination. To further enhance the performance of conventional MACE, it is necessary to provide quality-improved and three-dimensional images. The aim of this clinical study was to determine the efficacy and safety of novel three-dimensional MACE (3D MACE) for upper gastrointestinal and small bowel examination at once. METHODS: This was a prospective, single-center, non-randomized, and sequential examination study (KCT0007114) at Dongguk University Ilsan Hospital. Adult patients who visited for upper endoscopy were included. The study protocol was conducted in two stages. First, upper gastrointestinal examination was performed using 3D MACE, and a continuous small bowel examination was performed by conventional method of capsule endoscopy. Two hours later, an upper endoscopy was performed for comparison with 3D MACE examination. The primary outcome was confirmation of major gastric structures (esophagogastric junction, cardia/fundus, body, angle, antrum, and pylorus). Secondary outcomes were confirmation of esophagus and duodenal bulb, accuracy for gastric lesions, completion of small bowel examination, 3D image reconstruction of gastric lesion, and safety. RESULTS: Fifty-five patients were finally enrolled. The examination time of 3D MACE was 14.84 ± 3.02 minutes and upper endoscopy was 5.22 ± 2.39 minutes. The confirmation rate of the six major gastric structures was 98.6% in 3D MACE and 100% in upper endoscopy. Gastric lesions were identified in 43 patients during 3D MACE, and 40 patients during upper endoscopy (Sensitivity 0.97). 3D reconstructed images were acquired for all lesions inspected by 3D MACE. The continuous small bowel examination by 3D MACE was completed in 94.5%. 3D MACE showed better overall satisfaction (3D MACE 9.55 ± 0.79 and upper endoscopy 7.75 ± 2.34, p<0.0001). There were no aspiration or significant adverse event or capsule retention in the 3D MACE examination. CONCLUSIONS: Novel 3D MACE system is more advanced diagnostic modality than the conventional MACE. And it is possible to perform serial upper gastrointestinal and small bowel examination as a non-invasive and one-step test. It would be also served as a bridge to pan-endoscopy.

Double balloon enteroscopy versus single balloon enteroscopy: A comparative study.

Device assisted enteroscopy (DAE) like the double balloon enteroscopy (DBE) and single balloon enteroscopy (SBE) are postulated to ease small bowel examination and performance of therapy. However, studies comparing the effectiveness of these 2 modalities have yielded varying results. The aim of this study is to compare the efficacy and safety of SBE and DBE. We retrospectively reviewed records of patients who underwent DBE (n = 82) or SBE (n = 45) for small bowel exam in our unit from January 2014 to January 2022. Our primary outcomes were to compare the technical success and diagnostic success rates between DBE and SBE. Our secondary outcomes were to compare the therapeutic success, and complication rates. The main indications were suspected GI bleeding (DBE 41.5% vs SBE 48.9%), iron deficiency anemia (DBE 9.8% vs SBE 4.4%) and small bowel lesions (DBE 28.0% vs SBE 44.4%) detected either from prior capsule endoscopy or radiological imaging. Majority of the enteroscopy exam was by antegrade approach (DBE 67.1% vs SBE 77.8%). We found no significant difference in the technical success (DBE 95.1% vs SBE 97.8%, P = .46), diagnostic success (DBE 69.5% vs SBE 77.8%, P = .36) and the therapeutic success rate (DBE 63.2% vs SBE 54.3%, P = .09) between the groups. Complications occurred in 1 case from each group (mucosal tear). None of the complications were major. In patients who underwent enteroscopy, the diagnostic and therapeutic performance of SBE is similar to DBE. Both procedures were safe with low complication rates.

Importance of Prenatal Diagnosis of Ileal Atresia in Gestational Diabetes Cases.

BACKGROUND Maldevelopment of the fetal bowel can result in the rare condition of intestinal atresia, which results in congenital bowel obstruction. This report describes a case of prenatal diagnosis of fetal ileal atresia at 22 weeks' gestation. CASE REPORT Here, we present a 24-year old woman who was 22 weeks into her first pregnancy when she underwent routine fetal ultrasound. She was diagnosed with gestational diabetes mellitus. Her body mass index was normal and she had normal weight gain. The ultrasonographic examination performed revealed a hyperechoic bowel and a small dilatation of the bowel. The couple was counselled for possible intestinal atresia and its postnatal implications. At 33 weeks of gestation, polyhydramnios appeared, and the intestinal distension was much more pronounced, with hyperechoic debris in the intestinal lumen (succus-entericus). After birth, surgery was performed and we concluded the patient had type II atresia, which was surgically treated. CONCLUSIONS This report has highlighted the importance of antenatal ultrasound in detecting fetal abnormalities, and has shown that rare conditions such as intestinal atresia can be accurately diagnosed and successfully managed. Surgical correction, if implemented promptly after stabilizing the general condition, can have a relatively good prognosis. Coexisting fetal ileal atresia and gestational diabetes mellitus are rare occurrences, which can make each condition even more difficult to treat.

Prevalence of metastases outside the liver and abdominal lymph nodes on 68Ga-DOTATOC-PET/CT in patients with small intestinal and pancreatic neuroendocrine tumours.

Metastases outside the liver and abdominal/retroperitoneal lymph nodes are nowadays detected frequently in patients with neuroendocrine tumours (NETs), owing to the high sensitivity of positron emission tomography (PET) with Gallium-68-DOTA-somatostatin analogues (68Ga-SSA) and concomitant diagnostic computed tomography (CT). Our aim was to determine the prevalence of extra-abdominal metastases on 68Ga-DOTATOC-PET/CT in a cohort of patients with small intestinal (Si-NET) and pancreatic NET (Pan-NET), as well as that of pancreatic metastasis in patients with Si-NET. Among 2090 patients examined by 68Ga-DOTATOC-PET/CT at two tertiary referral centres, a total of 1177 patients with a history of Si- or Pan-NET, were identified. The most recent 68Ga-DOTATOC-PET/CT report for each patient was reviewed, and the location and number of metastases of interest were recorded. Lesions outside the liver and abdominal nodes were found in 26% of patients (n = 310/1177), of whom 21.5% (255/1177) were diagnosed with Si-NET and 4.5% (55/1177) Pan-NET. Bone metastases were found in 18.4% (215/1177), metastases to Virchow's lymph node in 7.1% (83/1177), and lung/pleura in 4.8% (56/1177). In the subset of 255 Si-NET patients, 5.4% (41/255) manifested lesions in the pancreas, 1.5% in the breast (18/255), 1.3% in the heart (15/255) and 1% in the orbita (12/255). In Si-NET patients, the Ki-67 proliferation index was higher in those with ≥2 metastatic sites of interest, than with 1 metastatic site, (p <0.001). Overall, extra-abdominal or pancreatic metastases were more often found in patients with Si-NET (34%) than in those with Pan-NET (13%) (p <0.001). Bone metastases were 2.6 times more frequent in patients with Si-NET compared to Pan-NET patients (p <0.001). Lesions to the breast and orbita were encountered in almost only Si-NET patients. In conclusion, lesions outside the liver and abdominal nodes were detected in as many as 26% of the patients, with different prevalence and metastatic patterns in patients with Si-NET compared to Pan-NET. The impact of such metastases on overall survival and clinical decision-making needs further evaluation.

Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.

BACKGROUND: In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. METHODS: A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. RESULTS: The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CONCLUSIONS: CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.

AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study.

BACKGROUND: Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. METHODS: Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. FINDINGS: From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). INTERPRETATION: AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. FUNDING: ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.

Capsule endoscopy and panendoscopy: A journey to the future of gastrointestinal endoscopy.

In 2000, the small bowel capsule revolutionized the management of patients with small bowel disorders. Currently, the technological development achieved by the new models of double-headed endoscopic capsules, as miniaturized devices to evaluate the small bowel and colon [pan-intestinal capsule endoscopy (PCE)], makes this non-invasive procedure a disruptive concept for the management of patients with digestive disorders. This technology is expected to identify which patients will require conventional invasive endoscopic procedures (colonoscopy or balloon-assisted enteroscopy), based on the lesions detected by the capsule, i.e., those with an indication for biopsies or endoscopic treatment. The use of PCE in patients with inflammatory bowel diseases, namely Crohn's disease, as well as in patients with iron deficiency anaemia and/or overt gastrointestinal (GI) bleeding, after a non-diagnostic upper endoscopy (esophagogastroduodenoscopy), enables an effective, safe and comfortable way to identify patients with relevant lesions, who should undergo subsequent invasive endoscopic procedures. The recent development of magnetically controlled capsule endoscopy to evaluate the upper GI tract, is a further step towards the possibility of an entirely non-invasive assessment of all the segments of the digestive tract, from mouth-to-anus, meeting the expectations of the early developers of capsule endoscopy.

Computed tomographic diagnosis and clinical outcomes of small intestinal obstruction caused by trichobezoars in client-owned rabbits.

Small intestinal obstruction in rabbits, although demonstrated to be common, is still poorly characterized. The objective of this study was to report features of small intestinal obstruction caused by trichobezoars in rabbits upon CT and their clinical outcomes. Rabbits with a diagnosis of intestinal obstruction that underwent CT scanning were included in this study. From each CT scan, obstruction location, appearance, Hounsfield units, size (mm), volume (mm3), distance from the stomach, and diameter of the small intestine proximal and distal to it, were extracted. Nine rabbits were included in the study. All rabbits presented for anorexia and had a distended or enlarged stomach (7) or abdomen (2). CT scan showed a non-contrast-enhancing, ovoid, smoothly marginated structure with a hyperattenuating rim, heterogeneous center, and variable amount of centrally located gas, in the duodenum (5), the jejunum (2), or the ileum (2). The density of the trichobezoars ranged from -156 to 58 HU (median, -44 HU) and volume ranged from 86.1 to 633.8 mm3 (median, 320.6 mm3). Three rabbits underwent medical management. Two of them survived, and one of them died. Six rabbits underwent surgery, of which three were successfully discharged from the hospital. CT proved to be a valuable imaging modality in rabbits with a suspected intestinal obstruction to provide the exact location of the obstruction and the characteristics of the obstructive material. The location and size of the trichobezoar may have relevant clinical implications.

CT and MR Enterography in the Evaluation of Celiac Disease.

Celiac disease is a common inflammatory disease of the small bowel that induces mucosal intestinal lesions. The disease is mediated by an immune response and triggered by the ingestion of gluten in genetically predisposed individuals. Gluten contains gliadin, a component found mostly in wheat, barley, and rye. This process leads to gastrointestinal malabsorption with symptoms such as diarrhea, constipation, abdominal pain, and distention. It has a prevalence of 1%-2% in the general adult population, who present with symptoms at any age, but is more frequently found in adult women in the 3rd or 4th decade of life. Recognition of the disease has increased, but it remains a challenge to diagnose. CT and MR enterography are noninvasive studies used for evaluation of small bowel neoplasms and inflammatory small bowel pathologic conditions such as celiac disease. The authors review the spectrum of intestinal and extraintestinal findings of celiac disease at CT and MR enterography, as well as its complications, and the importance of recognizing certain imaging features that help in the diagnosis of celiac disease. More common and specific findings of celiac disease such as inversion of the jejunoileal fold pattern and mesenteric lymphadenopathy are reviewed. More uncommon entities that are more frequently associated with refractory or untreated celiac disease, such as ulcerative jejunoileitis, cavitary mesenteric lymph node syndrome, and malignancies including small bowel adenocarcinoma and lymphoma, are described. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. The slide presentation from the RSNA Annual Meeting is available for this article.

Abdominal computed tomography scoring systems and experienced radiologists in the radiological diagnosis of small bowel and mesenteric injury.

PURPOSE: Blunt bowel and/or mesenteric injury requiring surgery presents a diagnostic challenge. Although computed tomography (CT) imaging is standard following blunt trauma, findings can be nonspecific. Most studies have focused on the diagnostic value of CT findings in identifying significant bowel and/or mesenteric injury (sBMI). Some studies have described scoring systems to assist with diagnosis. Little attention, has been given to radiologist interpretation of CT scans. This study compared the discriminative ability of scoring systems (BIPS and RAPTOR) with radiologist interpretation in identifying sBMI. METHODS: We conducted a retrospective chart review of trauma patients with suspected sBMI. CT images were reviewed in a blinded fashion to calculate BIPS and RAPTOR scores. Sensitivity and specificity were compared between BIPS, RAPTOR, and the admission CT report with respect to identifying sBMI. RESULTS: One hundred sixty-two patients were identified, 72 (44%) underwent laparotomy and 43 (26.5%) had sBMI. Sensitivity and specificity were: BIPS 49% and 87%, AUC 0.75 (0.67-0.81), P < 0.001; RAPTOR 46% and 82%, AUC 0.72 (0.64-0.79), P < 0.001; radiologist impression 81% and 71%, AUC 0.82(0.75-0.87), P < 0.001. The discriminative ability of the radiologist impression was higher than RAPTOR (P = 0.04) but not BIPS (P = 0.13). There was not a difference between RAPTOR vs. BIPS (P = 0.55). CONCLUSION: Radiologist interpretation of the admission CT scan was discriminative of sBMI. Although surgical vigilance, including evaluation of the CT images and patient, remains fundamental to early diagnosis, the radiologist's impression of the CT scan can be used in clinical practice to simplify the approach to patients with abdominal trauma.

Prediction of bowel necrosis by reduced bowel wall enhancement in closed-loop small bowel obstruction: Quantitative methods.

PURPOSE: To assess diagnostic performance and reproducibility of reduced bowel wall enhancement evaluated by quantitative methods using CT to identify bowel necrosis among closed-loop small bowel obstruction (CL-SBO) patients. METHODS: This retrospective single-center study included patients who diagnosed with CL-SBO caused by adhesion or internal hernia during January 2016 and May 2022. Patients were divided into necrotic group (n = 41) and non-necrotic group (n = 67) according to surgical exploration and postoperative pathology. Two doctors independently measured the attenuation of bowel wall and consensus was reached through panel discussion with a third gastrointestinal radiologist. Reduced bowel wall enhancement was assessed by four quantitative methods. Univariate analyses were used to evaluate the association between each method and bowel necrosis, and kappa/intraclass correlation coefficient values were used to assess interobserver agreement. Diagnostic performance parameters were calculated for each method. RESULTS: Reduced bowel wall enhancement in arterial phase (OR 8.98, P < 0.0001), reduced bowel wall enhancement in portal phase (OR 16.84, P < 0.001), adjusted reduced bowel wall enhancement in arterial phase (OR 29.48, P < 0.001), adjusted reduced bowel wall enhancement in portal phase (OR 145.69, P < 0.001) were significantly associated with bowel necrosis. Adjusted reduced bowel wall enhancement in portal phase had the best diagnostic performance (AUC: 0.92; Youden index: 0.84; specificity: 94.03 %) and interobserver agreement (kappa value of 0.59-0.73) to predict bowel necrosis. CONCLUSION: When assessing reduced bowel enhancement to predict bowel necrosis among CL-SBO patients, using unenhanced CT images and proximal dilated loop as standard references in portal phase is the most accurate quantitative method among those tested.

Reading of small bowel capsule endoscopy after frame reduction using an artificial intelligence algorithm.

OBJECTIVES: Poorly visualized images that appear during small bowel capsule endoscopy (SBCE) can confuse the interpretation of small bowel lesions and increase the physician's workload. Using a validated artificial intelligence (AI) algorithm that can evaluate the mucosal visualization, we aimed to assess whether SBCE reading after the removal of poorly visualized images could affect the diagnosis of SBCE. METHODS: A study was conducted to analyze 90 SBCE cases in which a small bowel examination was completed. Two experienced endoscopists alternately performed two types of readings. They used the AI algorithm to remove poorly visualized images for the frame reduction reading (AI user group) and conducted whole frame reading without AI (AI non-user group) for the same patient. A poorly visualized image was defined as an image with < 50% mucosal visualization. The study outcomes were diagnostic concordance and reading time between the two groups. The SBCE diagnosis was classified as Crohn's disease, bleeding, polyp, angiodysplasia, and nonspecific finding. RESULTS: The final SBCE diagnoses between the two groups showed statistically significant diagnostic concordance (k = 0.954, p < 0.001). The mean number of lesion images was 3008.5 ± 9964.9 in the AI non-user group and 1401.7 ± 4811.3 in the AI user group. There were no cases in which lesions were completely removed. Compared with the AI non-user group (120.9 min), the reading time was reduced by 35.6% in the AI user group (77.9 min). CONCLUSIONS: SBCE reading after reducing poorly visualized frames using the AI algorithm did not have a negative effect on the final diagnosis. SBCE reading method integrated with frame reduction and mucosal visualization evaluation will help improve AI-assisted SBCE interpretation.

Automated classification of ulcerative lesions in small intestine using densenet with channel attention and residual dilated blocks.

Objective. Ulceration of the small intestine, which has a high incidence, includes Crohn's disease (CD), intestinal tuberculosis (ITB), primary small intestinal lymphoma (PSIL), cryptogenic multifocal ulcerous stenosing enteritis (CMUSE), and non-specific ulcer (NSU). However, the ulceration morphology can easily be misdiagnosed through enteroscopy.Approach. In this study, DRCA-DenseNet169, which is based on DenseNet169, with residual dilated blocks and a channel attention block, is proposed to identify CD, ITB, PSIL, CMUSE, and NSU intelligently. In addition, a novel loss function that incorporates dynamic weights is designed to enhance the precision of imbalanced datasets with limited samples. DRCA-Densenet169 was evaluated using 10883 enteroscopy images, including 5375 ulcer images and 5508 normal images, which were obtained from the Shanghai Changhai Hospital.Main results. DRCA-Densenet169 achieved an overall accuracy of 85.27% ± 0.32%, a weighted-precision of 83.99% ± 2.47%, a weighted-recall of 84.36% ± 0.88% and a weighted-F1-score of 84.07% ± 2.14%.Significance. The results demonstrate that DRCA-Densenet169 has high recognition accuracy and strong robustness in identifying different types of ulcers when obtaining immediate and preliminary diagnoses.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.

BACKGROUND: Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges. AIM: To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups. METHODS: The proposed model represents a two-stage method that combined image classification with object detection. First, we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images, normal SB mucosa images, and invalid images. Then, the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding, and the location of the lesion was marked. We constructed training and testing sets and compared model-assisted reading with physician reading. RESULTS: The accuracy of the model constructed in this study reached 98.96%, which was higher than the accuracy of other systems using only a single module. The sensitivity, specificity, and accuracy of the model-assisted reading detection of all images were 99.17%, 99.92%, and 99.86%, which were significantly higher than those of the endoscopists' diagnoses. The image processing time of the model was 48 ms/image, and the image processing time of the physicians was 0.40 ± 0.24 s/image (P < 0.001). CONCLUSION: The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images, which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Primary small intestine angiosarcoma mimicking Crohn's disease.

A man in his 40s presented to an emergency department after experiencing worsening abdominal pain for 2 days. Contrast-enhanced CT of the abdomen and pelvis revealed circumferential mural thickening and luminal narrowing of the distal ileum and upstream dilatation of the small intestine, indicating small intestine obstruction. This prompted emergency laparotomy, where two lesions in the distal ileum were identified as the source of his bowel obstruction and resected. Immunohistochemistry of the resected segment revealed a primary small intestine angiosarcoma acting positively for vascular markers ERG and CD31. A subsequent positron emission tomography (PET) scan revealed positive mediastinal metastatic lymphadenopathy without organ metastases.Following his surgery, the patient recovered well and was promptly referred to an oncology unit at a specialised health centre for further treatment. Primary small intestine angiosarcoma is a rare entity in which patients present with non-specific symptoms requiring prompt tissue diagnosis to facilitate multidisciplinary management.