Due to similar clinical manifestations and imaging signs, differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease (CD) is a challenge in clinical practice.

To investigate the ability of radiomics combined with machine learning methods to differentiate PIL from CD.

We collected contrast-enhanced computed tomography (CECT) and clinical data from 120 patients form center 1. A total of 944 features were extracted single-phase images of CECT scans. Using the last absolute shrinkage and selection operator model, the best predictive radiographic features and clinical indications were screened. Data from 54 patients were collected at center 2 as an external validation set to verify the robustness of the model. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity were used for evaluation.

A total of five machine learning models were built to distinguish PIL from CD. Based on the results from the test group, most models performed well with a large area under the curve (AUC) (> 0.850) and high accuracy (> 0.900). The combined clinical and radiomics model (AUC = 1.000, accuracy = 1.000) was the best model among all models.

Based on machine learning, a model combining clinical data with radiologic features was constructed that can effectively differentiate PIL from CD.

Differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease (CD) is a challenge in clinical diagnosis.

To investigate the validity of the nomogram based on clinical and computed tomography (CT) features to identify PIL and CD.

This study retrospectively analyzed laboratory parameters, demographic characteristics, clinical manifestations, and CT imaging features of PIL and CD patients from two centers. Univariate logistic analysis was performed for each variable, and laboratory parameter model, clinical model and imaging features model were developed separately. Finally, a nomogram was established. All models were evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA).

This study collected data from 121 patients (PIL = 69, CD = 52) from Center 1. Data from 43 patients (PIL = 24, CD = 19) were collected at Center 2 as an external validation cohort to validate the robustness of the model. Three models and a nomogram were developed to distinguish PIL from CD. Most models performed well from the external validation cohort. The nomogram showed the best performance with an AUC of 0.921 (95% CI: 0.838-1.000) and sensitivities, specificities, and accuracies of 0.945, 0.792, and 0.860, respectively.

A nomogram combining clinical data and imaging features was constructed, which can effectively distinguish PIL from CD.

Radiological procedures play a crucial role in the diagnosis of small bowel disease. Due to a broad and quite nonspecific spectrum of symptoms, clinical evaluation is often difficult, and endoscopic procedures require significant manpower, are time-consuming and expensive. In contrast, radiologic imaging can provide important information about morphologic and functional variations of the small bowel and help to identify various disease entities, such as inflammation, tumors, vascular problems, and obstruction.

The most common radiological modalities in small bowel diagnostics include ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and fluoroscopy. Each of these modalities has its own advantages and limitations, and the choice of imaging modality depends on clinical symptoms and suspected diagnosis in addition to availability.

In recent years, significant progress has been made, especially in cross-sectional imaging modalities, as a result of new and further technical developments.

These range from increasing detail resolution to functional and molecular imaging techniques that go far beyond simple morphology. In addition, information technology (IT) applications, which include artificial intelligence and radiomics, are assuming an increasing role.

Many of the methods mentioned are still in early stages and need to be further developed for daily practice, but some have already found their way into clinical routine.

The aim of this work is to provide a review of the most important disease entities of the small intestine, including new and innovative diagnostic approaches.

A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification.

We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification.

PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality.

In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups.

The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required.

PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.

Small bowel neoplasms are rare and account for 3-6% of all gastrointestinal neoplasms. For the diagnosis of small bowel neoplasms, differentiating normal bowel tissue from tumor is critical and depends on imaging modality and scanning techniques. The detection and characterization of small bowel neoplasms have recently improved with the advance of computed tomography (CT) technology. Post-contrast multiphasic CT is an aid to detection and recognition of the vascular nature of small bowel neoplasms. Understanding the typical post-contrast multiphasic CT features of small bowel neoplasms is important because of overlapping features and the necessity of evaluating associated complications and metastases to lymph node and other organs. However, accurate classification of pathologies is still challenging in clinical practice. Texture analysis can quantify complex mathematical patterns within the gray-level distribution of the pixels and voxels of digital images, and texture analysis of the post-contrast multidetector CT data of various tumors has been attracting attention in recent years. The aim of this article is to provide a comprehensive guide to the relevant imaging features for different types of malignant small bowel neoplasms. J. Med. Invest. 69 : 19-24, February, 2022.