Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer

Spectral CT-based nomogram for evaluation of neoadjuvant chemotherapy response in esophageal squamous cell carcinoma

OBJECTIVES

To establish a spectral CT-based nomogram for predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study included 172 patients with ESCC who underwent spectral CT scans before NAC followed by resection. Based on postoperative tumor regression grades (TRG), 34% (58) of patients were responsive (TRG1) and 66% (114) were non-responsive (TRG2-3). The data was divided into a primary set of 120 and a validation set of 52, maintaining a 7:3 random ratio. Measurements included iodine concentration (IC), normalized iodine concentration (nIC), CT40kev, CT70kev, spectral attenuation curve slope (λHU), and effective atomic number (Zeff) during non-contrast and venous phases (VP). Clinicopathologic characteristics were collected. Univariable and multivariable logistic regressions identified independent predictors of NAC response. The model was visualized using nomograms, and its efficacy was assessed via receiver operating characteristic (ROC) curves.

RESULTS

Multivariable logistic regression analysis identified the neutrophil-to-lymphocyte ratio (NLR), clinical stage, ZeffVP, and nICVP as independent predictors of NAC response. The nomogram incorporating all four independent predictors, outperformed spectral CT and the clinical model with the highest AUCs of 0.825 (95% CI: 0.746-0.895) for the primary set and 0.794 (95% CI: 0.635-0.918) for the validation set (DeLong test: all p < 0.05).

CONCLUSIONS

The spectral CT and clinical models were useful in predicting NAC response in ESCC patients. Combining spectral CT imaging parameters and clinicopathologic characteristics in a nomogram improved predictive accuracy.

KEY POINTS

Question Developing a non-invasive, practical tool to predict ESCC's response to chemotherapy is crucial and has not yet been done. Findings This nomogram, incorporating clinicopathologic characteristics and spectral CT-derived parameters, predicted NAC response in ESCC patients. Clinical relevance This spectral CT-based nomogram is a non-invasive and easily obtainable tool for accurately predicting ESCC response to NAC, aiding clinicians in personalized treatment planning.

The clinical applications of dual-layer spectral detector CT in digestive system diseases

OBJECTIVE

Dual-layer spectral detector CT (DLCT) has several advantages in clinical practice, this study aims to reveal the clinical applications of DLCT in digestive system diseases.

MATERIALS AND METHODS

We searched PubMed and Cochrane Reviews for articles published from January 1, 2010 to May 31, 2024, using the terms "dual-layer spectral detector CT" or "dual-layer CT" combined with "hepatic fat" or "hepatic fibrosis" "hepatocellular carcinoma" or "pancreatic ductal adenocarcinoma" or "pancreatic neuroendocrine tumors" or "gastric cancer" or "colorectal cancer" or "Crohn's disease" or "bowel ischemia" or "acute abdominal conditions".

RESULTS

DLCT consists of a top layer sensitive to lower-energy photons and a bottom layer sensitive to higher-energy photons. This configuration enables simultaneous acquisition of two energy spectra from a single X-ray beam ensuring consistent spatial alignment and temporal resolution. Spectral raw images allow image post-processing to improve image quality, reduce radiation doses and contrast media doses, and generate multiple quantitative parameters. It has broad potential for early detection, accurate staging, efficacy assessment, and prognosis prediction of liver, pancreatic, and gastrointestinal diseases, as well as for the assessment of digestive system vasculature.

CONCLUSIONS

DLCT not only provides valuable information for the clinical diagnosis and therapeutic effect evaluation of digestive system diseases but also may play a more important role in the overall management of digestive diseases and in the decision-making of individualized medicine.

KEY POINTS

Question What are the advantages of DLCT compared to traditional single-energy CT in the early detection, staging, and therapeutic evaluation of digestive system diseases? Findings DLCT enhances image quality, improves tissue characterization, and allows for multi-parametric analysis, making it superior in detecting and evaluating liver, pancreatic, and gastrointestinal diseases. Clinical relevance DLCT provides high-quality, multi-parametric imaging that improves the accuracy of diagnosing digestive diseases, facilitates more precise treatment planning, and enhances monitoring of treatment response, ultimately contributing to better patient management and prognosis.

Prognosis and Treatment of Gastric Cancer: A 2024 Update

Due to the high death rate associated with gastric cancer, a great deal of research has been conducted on this disease. The goal of this paper was to start a trimestral review of 2024 for the year that had just started. The scientific literature from 1 January 2024 was chosen with consideration of the the guidelines of the European Society of Medical Oncology (ESMO), which are updated with new findings but not systematically reviewed annually. We used the search term "gastric cancer" to find the most current publications in the PubMed database related to the prognosis and treatment of gastric cancer. As previously said, the only articles that satisfied the inclusion criteria were those from 2024. Articles with case reports were eliminated since they had nothing to do with our research. The treatment of gastric cancer is the focus of the majority of articles from 2024. The primary research axes include surgery and immunonutrition, immunotherapy and Helicobacter pylori, and therapeutic targets. Patients with GC may experience less psychological, social, and financial hardship if the recently identified markers discovered in circulation are better assessed and validated. This could be achieved by either including the markers in an artificial intelligence-based diagnostic score or by using them in conjunction with traditional diagnostic methods. Due to the rising death rate associated with GC, funding for research into diagnosis, prognosis, therapy, and therapeutic targets is essential.