The complication of hydropneumothorax and colovesical fistula is rare, especially in patients with tuberculosis (TB) and COVID-19. This particular situation poses a management difficulty, and can significantly threaten the patient\'s life without a clear diagnosis and timely treatment. We report a 28-year-old woman with pulmonary and intestinal TB with COVID-19 complicated with hydropneumothorax and colovesical fistula (CVF) which worsened her condition. Treatment for this patient was given according to the diagnosis. Her condition improved and she was discharged after 30 days of hospitalization, while elective surgery for CVF was not performed because there were no clinical symptoms complained of by this patient after completing TB treatment (9 months after hospital discharge). This case report highlights the importance of considering pulmonary and intestinal TB with COVID-19 as the cause of hydropneumothorax and CVF. Early and complex diagnosis is essential for proper management, as well as the efficacy of medical therapy and treatment for controlling such advanced stages of the disease. A complex condition with many symptoms can overlap with other diseases. Clinicians should consider the clinical symptoms, radiological imaging, and standard or supporting examination for accurate diagnosis to find the etiology of the diseases. Complete treatment for TB should be considered as the treatment choice (nonsurgical therapy) for CVF caused by TB before deciding on surgical intervention.

The coexistence of carcinoma of the colon and tuberculosis (TB) represents a rare and intricate clinical scenario. It poses significant challenges in both diagnosis and management. Clinical prediction of this coexistence is challenging since the clinical features of these two conditions are often similar. Likewise, the radiology is not decisive because of the significant overlap in the image findings of carcinoma and TB. A conclusive diagnosis relies on histopathological evidence of both malignancy and TB. Here, we report a case of a 58-year-old female who presented with chronic abdominal pain. Computed tomography showed the presence of a mass in the cecum. Histopathology of tissue retrieved through colonoscopy was indicative of features of both TB and adenocarcinoma of the cecum. Mycobacterium tuberculosis was detected in the tissue by cartridge-based nucleic acid amplification test. The patient was initiated on antitubercular treatment. She underwent surgical resection of the mass and is planned for adjuvant chemotherapy.

BACKGROUND: The diagnosis of intestinal tuberculosis is challenging even nowadays. This study aims to report the positivity rates of new diagnostic methods such as immunohistochemistry and Real-Time Polymerase Chain Reaction in patients with intestinal tuberculosis, as well as describe the pathological and endoscopic features of intestinal tuberculosis in our population.
METHODS: This was a retrospective observational study conducted in patients diagnosed with intestinal tuberculosis, between 2010 to 2023 from the Hospital Nacional Daniel Alcides Carrion and a Private Pathology Center, both located in Peru. Clinical data was obtained, histologic features were independently re-evaluated by three pathologists; and immunohistochemistry and real-time Polymerase Chain Reaction evaluation were performed. The 33 patients with intestinal tuberculosis who fulfilled the inclusion criteria were recruited.
RESULTS: Immunohistochemistry was positive in 90.9% of cases, while real-time Polymerase Chain Reaction was positive in 38.7%. The ileocecal region was the most affected area (33.3%), and the most frequent endoscopic appearance was an ulcer (63.6%). Most of the granulomas were composed solely of epithelioid histiocytes (75.8%). Crypt architectural disarray was the second most frequent histologic finding (78.8%) after granulomas, but most of them were mild.
CONCLUSIONS: Since immunohistochemistry does not require an intact cell wall, it demonstrates higher sensitivity compared to Ziehl-Neelsen staining. Therefore, it could be helpful for the diagnosis of paucibacillary tuberculosis.

OBJECTIVE: Differentiating intestinal tuberculosis (ITB) from Crohn\'s disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD.
METHODS: Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong\'s test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models.
RESULTS: The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68-0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71-0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48-0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49-0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78-1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility.
CONCLUSIONS: Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

UNASSIGNED: Colonoscopy is useful in diagnosing intestinal tuberculosis. However, the terminal ileum is generally not examined during routine colonoscopy. Therefore, even with colonoscopy, the diagnosis can be missed in patients with lesions confined to the terminal ileum. Herein, we report the case of an asymptomatic patient with intestinal tuberculosis, in whom a colonoscope insertion into the terminal ileum led to the diagnosis.
UNASSIGNED: An asymptomatic 71-year-old man visited our hospital for a colonoscopy after a positive fecal occult blood test.
UNASSIGNED: Colonoscopy revealed diffuse edematous and erosive mucosa in the terminal ileum. Mycobacterium tuberculosis was detected by polymerase chain reaction and culture of biopsy specimens from the erosions, leading to the diagnosis of intestinal tuberculosis. The patient was treated with antitubercular agents for 6 months, and a follow-up colonoscopy revealed healing of the lesions.
UNASSIGNED: Asymptomatic intestinal tuberculosis may occasionally be detected on colonoscopy following a positive fecal occult blood test and is sometimes confined to the terminal ileum. Therefore, clinicians should consider intestinal tuberculosis in the differential diagnosis of the causes of positive fecal occult blood test results and perform colonoscopies, including observation of the terminal ileum.

BACKGROUND: Crohn\'s disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed.
OBJECTIVE: To develop a method to identify CD and ITB with high accuracy, specificity, and speed.
METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.
RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB.
CONCLUSIONS: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Tuberculosis (TB) remains a significant global health concern, with millions affected worldwide each year. Extrapulmonary TB, particularly involving the digestive tract and central nervous system, poses distinctive difficulties in both diagnosis and treatment. We report a case involving a 15-year-old girl with a history of intestinal TB on anti-tuberculous therapy who presented with symptoms suggestive of meningitis, along with abdominal pain and distension. Our initial suspicion was tuberculous meningitis, considering the underlining abdominal TB, which was later supported by cerebrospinal fluid analysis showing lymphocytic-predominant pleocytosis and positive acid-fast bacilli staining. Concurrently, the patient developed hemodynamic instability and severe abdominal pain, which on repeat X-rays of the abdomen showed air under the diaphragms, prompting surgical exploration and revealing multiple ileal perforations. Histopathological examination confirmed TB as the cause of perforation. This case highlights the diagnostic and therapeutic complexities of concurrent tuberculous meningitis and intestinal TB perforation. Early recognition and interdisciplinary management are crucial for optimal patient outcomes.

This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn\'s disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman\'s correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.

UNASSIGNED: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn\'s disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements.
UNASSIGNED: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets.
UNASSIGNED: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61).
UNASSIGNED: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

Crohn\'s disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of surgical resection specimens to distinguish CD from ITB. Overall, 1973 WSI from 85 cases from 3 centers were obtained. The DL model was established in internal training and validated in external test cohort, evaluated by area under receiver operator characteristic curve (AUC). Diagnostic results of pathologists were compared with those of the DL model using DeLong\'s test. DL model had case level AUC of 0.886, 0.893 and slide level AUC of 0.954, 0.827 in training and test cohorts. Attention maps highlighted discriminative areas and top 10 features were extracted from CD and ITB. DL model\'s diagnostic efficiency (AUC = 0.886) was better than junior pathologists (*1 AUC = 0.701, P = 0.088; *2 AUC = 0.861, P = 0.788) and inferior to senior GI pathologists (*3 AUC = 0.910, P = 0.800; *4 AUC = 0.946, P = 0.507) in training cohort. In the test cohort, model (AUC = 0.893) outperformed senior non-GI pathologists (*5 AUC = 0.782, P = 0.327; *6 AUC = 0.821, P = 0.516). We developed a DL model for the classification of CD and ITB, improving pathological diagnosis accuracy effectively.