New insights into the pathogenesis of necrotizing enterocolitis and the dawn of potential therapeutics

Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs

BACKGROUND

Radiographic diagnosis of necrotizing enterocolitis (NEC) is challenging. Deep learning models may improve accuracy by recognizing subtle imaging patterns. We hypothesized it would perform with comparable accuracy to that of senior surgical residents.

METHODS

This cohort study compiled 494 anteroposterior neonatal abdominal radiographs (214 images NEC, 280 other) and randomly divided them into training, validation, and test sets. Transfer learning was utilized to fine-tune a ResNet-50 deep convolutional neural network (DCNN) pre-trained on ImageNet. Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps visualized image regions of greatest relevance to the pretrained neural network. Senior surgery residents at a single institution examined the test set. Resident and DCNN ability to identify pneumatosis on radiographic images were measured via area under the receiver operating curves (AUROC) and compared using DeLong's method.

RESULTS

The pretrained neural network achieved AUROC of 0.918 (95% CI, 0.837-0.978) with an accuracy of 87.8% with five false negative and one false positive prediction. Heatmaps confirmed appropriate image region emphasis by the pretrained neural network. Senior surgical residents had a median area under the receiver operating curve of 0.896, ranging from 0.778 (95% CI 0.615-0.941) to 0.991 (95% CI 0.971-0.999) with zero to five false negatives and one to eleven false positive predictions. The deep convolutional neural network performed comparably to each surgical resident's performance (p > 0.05 for all comparisons).

CONCLUSIONS

A deep convolutional neural network trained to recognize pneumatosis can quickly and accurately assist clinicians in promptly identifying NEC in clinical practice.

LEVEL OF EVIDENCE

III (study type: Study of Diagnostic Test, study of nonconsecutive patients without a universally applied "gold standard").

Chromatin as alarmins in necrotizing enterocolitis

Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease primarily affecting premature neonates, marked by poorly understood pro-inflammatory signaling cascades. Recent advancements have shed light on a subset of endogenous molecular patterns, termed chromatin-associated molecular patterns (CAMPs), which belong to the broader category of damage-associated molecular patterns (DAMPs). CAMPs play a crucial role in recognizing pattern recognition receptors and orchestrating inflammatory responses. This review focuses into the realm of CAMPs, highlighting key players such as extracellular cold-inducible RNA-binding protein (eCIRP), high mobility group box 1 (HMGB1), cell-free DNA, neutrophil extracellular traps (NETs), histones, and extracellular RNA. These intrinsic molecules, often perceived as foreign, have the potential to trigger immune signaling pathways, thus contributing to NEC pathogenesis. In this review, we unravel the current understanding of the involvement of CAMPs in both preclinical and clinical NEC scenarios. We also focus on elucidating the downstream signaling pathways activated by these molecular patterns, providing insights into the mechanisms that drive inflammation in NEC. Moreover, we scrutinize the landscape of targeted therapeutic approaches, aiming to mitigate the impact of tissue damage in NEC. This in-depth exploration offers a comprehensive overview of the role of CAMPs in NEC, bridging the gap between preclinical and clinical insights.

Necrotizing enterocolitis: current understanding of the prevention and management

Necrotizing enterocolitis (NEC) is one of the diseases in neonates, with a high morbidity and mortality rate, especially in preterm infants. This review aimed to briefly introduce the latest epidemiology, susceptibility factors, and clinical diagnosis and presentation of NEC. We also organized new prevention strategies by risk factors according to different pathogeneses and then discussed new treatment methods based on Bell's staging and complications, and the classification of mild to high severity based on clinical and imaging manifestations. Such a generalization will help clinicians and researchers to gain a deeper understanding of the disease and to conduct more targeted classification, grading prevention, and exploration. We focused on prevention and treatment of the early and suspected stages of NEC, including the discovery of novel biomarkers and drugs to control disease progression. At the same time, we discussed its clinical application, future development, and shortcomings.