Differentiating peritoneal tuberculosis and peritoneal carcinomatosis based on a machine learning model with CT: a multicentre study

Imaging in malignant peritoneal neoplasms

Peritoneal malignancies encompass a diverse range of tumors originating within the peritoneum, including primary tumors such as mesothelioma and primary serous peritoneal carcinoma or secondary tumors resulting from the spread of cancers from gastrointestinal, gynecological, and extra-abdominal sources. The traditional approach of palliative care for these malignancies is being replaced by a multimodal strategies that integrates surgery with systemic or intraperitoneal chemotherapy. Notably, cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy has shown significant improvements in survival rates. Imaging is crucial in the multidisciplinary management of these tumors, aiding in diagnosis, staging, restaging, and monitoring therapy response. It is also vital for appropriate patient selection, using the acronym "PAUSE", which involves assessing tumor burden via the peritoneal carcinomatosis index, evaluating patients pre- and post-therapy, detecting complications following therapy, and predicting treatment outcomes. This review explores the imaging manifestations of peritoneal malignancies, distinguishing them from various mimics, and underscores the importance of imaging modalities such as CT, MRI, PET/CT, and PET/MRI in effective decision-making and management.

Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis

Peritoneal carcinomatosis (PC) is a type of secondary cancer which is not sensitive to conventional intravenous chemotherapy. Treatment strategies for PC are usually palliative rather than curative. Recently, artificial intelligence (AI) has been widely used in the medical field, making the early diagnosis, individualized treatment, and accurate prognostic evaluation of various cancers, including mediastinal malignancies, colorectal cancer, lung cancer more feasible. As a branch of computer science, AI specializes in image recognition, speech recognition, automatic large-scale data extraction and output. AI technologies have also made breakthrough progress in the field of peritoneal carcinomatosis (PC) based on its powerful learning capacity and efficient computational power. AI has been successfully applied in various approaches in PC diagnosis, including imaging, blood tests, proteomics, and pathological diagnosis. Due to the automatic extraction function of the convolutional neural network and the learning model based on machine learning algorithms, AI-assisted diagnosis types are associated with a higher accuracy rate compared to conventional diagnosis methods. In addition, AI is also used in the treatment of peritoneal cancer, including surgical resection, intraperitoneal chemotherapy, systemic chemotherapy, which significantly improves the survival of patients with PC. In particular, the recurrence prediction and emotion evaluation of PC patients are also combined with AI technology, further improving the quality of life of patients. Here we have comprehensively reviewed and summarized the latest developments in the application of AI in PC, helping oncologists to comprehensively diagnose PC and provide more precise treatment strategies for patients with PC.

CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study

PURPOSE

Peritoneal carcinomatosis (PC) and peritoneal tuberculosis (PTB) have similar clinical and radiologic imaging features, which make it very difficult to differentiate between the two entities clinically. Our aim was to determine if the CT textural parameters of omental lesions among patients with PC were different from those with PTB.

METHODS

All patients who had undergone omental biopsy at our institution from January 2010 to December 2018 and had a tissue diagnosis of PC or PTB were eligible for inclusion. Patients who did not have a contrast-enhanced CT abdomen within one month of the omental biopsy were excluded. A region of interest (ROI) was manually drawn over omental lesions and radiomic features were extracted using open-source LIFEx software. Statistical analysis was performed to compare mean differences in CT texture parameters between the PC and PTB groups.

RESULTS

A total of 66 patients were included in the study of which 38 and 28 had PC and PTB, respectively. Omental lesions in patients with PC had higher mean radiodensity (mean difference: +32.4; p = 0.001), higher mean entropy (mean difference: +0.11; p < 0.001), and lower mean energy (mean difference: -0.024; p = 0.001) compared to those in PTB. Additionally, omental lesions in the PC group had lower gray-level co-occurrence matrix (GLCM) homogeneity (mean difference: -0.073; p < 0.001) and higher GLCM dissimilarity (mean difference: +0.480; p < 0.001) as compared to the PTB group.

CONCLUSION

CT texture parameters of omental lesions differed significantly between patients with PTB and those with PC, which may help clinicians in differentiating between the two entities.

Clinical and Radiological Parameters to Discriminate Tuberculous Peritonitis and Peritoneal Carcinomatosis

It is challenging to differentiate between tuberculous peritonitis and peritoneal carcinomatosis due to their insidious nature and intersecting symptoms. Computed tomography (CT) is the modality of choice in evaluating diffuse peritoneal disease. We conducted an ambispective analysis of patients suspected as having tuberculous peritonitis or peritoneal tuberculosis between Jan 2020 to Dec 2021. The study aimed to identify the clinical and radiological features differentiating the two entities. We included 44 cases of tuberculous peritonitis and 45 cases of peritoneal carcinomatosis, with a median age of 31.5 (23.5-40) and 52 (46-61) years, respectively (p ≤ 0.001). Fever, past history of tuberculosis, and loss of weight were significantly associated with tuberculous peritonitis (p ≤ 0.001, p = 0.038 and p = 0.001). Pain in the abdomen and history of malignancy were significantly associated with peritoneal carcinomatosis (p = 0.038 and p ≤ 0.001). Ascites was the most common radiological finding. Loculated ascites, splenomegaly and conglomeration of lymph nodes predicted tuberculous peritonitis significantly (p ≤ 0.001, p = 0.010, p = 0.038). Focal liver lesion(s) and nodular omental involvement were significantly associated with peritoneal carcinomatosis (p = 0.011, p = 0.029). The use of clinical features in conjunction with radiological findings provide better diagnostic yields because of overlapping imaging findings.