Background and Purpose: Several studies have proposed that the advanced lung cancer inflammation index (ALI), a new inflammation-related index, can be used for the prognosis assessment of various malignancies. However, few studies have reported its prognostic value in colorectal cancer (CRC). Therefore, this study explored the relationship between ALI and outcomes in CRC patients. Methods: A total of 662 CRC patients who underwent surgery between 2012 and 2014 were included. The ALI was defined as: body mass index × serum albumin/neutrophil to lymphocyte ratio. The X-tile program identified the optimal cut-off value of ALI. Logistic regression analyses determined factors affecting postoperative complications. The Kaplan–Meier method and Cox proportional hazards analyses evaluated potential prognostic factors. Results: The optimal cut-off of ALI in males and females were 31.6 and 24.4, respectively. Low-ALI was an independent risk factor for postoperative complications in CRC patients (odds ratio: 1.933, 95% CI [1.283–2.911], p = 0.002). Low-ALI groups also had significantly lower progression-free survival (PFS) and overall survival (OS), when compared with the high-ALI group, especially at advance tumor stages. Using multivariate analysis, ALI was determined as an independent prognostic factor for PFS (hazard ratio: 1.372, 95% CI [1.060–1.777], p = 0.016) and OS (hazard ratio: 1.453, 95% confidence interval: 1.113–1.898, p = 0.006). Conclusion: ALI is an independent predictor of short and long-term outcomes in CRC patients, especially at advance tumor stages. The ALI-based nomograms can provide accurate and individualized prediction of postoperative complication risk and survival for CRC patients.

The platelet-to-albumin ratio (PAR) was developed to evaluate inflammatory and nutritional status among patients. The primary goal of the current study was to gain insight into the prognostic role of PAR in critically ill patients with colorectal cancer (CRC). The secondary aim was to develop and verify a clinical model including PAR for the prediction of 28-day mortality. This observational, multicenter study used data from the Medical Information Mart for Intensive Care (MIMIC) IV, e-ICU databases, and Union cohort. Data from 776 critically ill patients with CRC were from the e-ICU database, 219 from the MIMC-IV database, and 135 from the Wuhan Union Hospital. Propensity score matching (PSM) analysis, along with inverse probability treatment weighting, was used to control the influence of confounding factors. Support vector machine (SVM) and LASSO Cox models were then applied to identify significant metrics associated with 28-day mortality in the test cohort. Receiver operating curve (ROC) analysis, along with sensitivity and specificity, was measured to assess the predictive performances of PAR and the survival nomogram. The threshold value for PAR was 8.6, and patients with high PAR (≥8.6) experienced higher 28-day mortality compared to those with low PAR (<8.6). ROC curve analyses revealed that the discriminative ability of PAR was better than platelet count and albumin alone. LASSO Cox regression along with SVM identified six significant metrics associated with 28-day mortality in critically ill patients with CRC, including PAR. The C-index of the critically ill CRC nomogram was 0.802 (0.744–0.859) in the e-ICU training cohort, 0.839 (0.779–0.899) in the e-ICU validation cohort, 0.787 (0.695–0.879) in the MIMIC-IV cohort, and 0.767 (0.703–0.831) in the Union cohort. PAR is a simple score that combines inflammatory and nutritional status. PAR was a reliable index to predict short-term survival outcome of critically ill patients with CRC. Moreover, a clinical nomogram incorporating PAR exhibited satisfactory performance for predicting 28-day mortality of critically ill patients with CRC.

Ultrasound computed tomography (USCT) has emerged as a promising tool for quantitative assessment of musculoskeletal (MSK) diseases. However, the accuracy of echo intensity—a key imaging biomarker—is often compromised by non-optimal imaging conditions, such as probe tilt and limb eccentricity. In this study, we propose a novel echo intensity correction method for USCT that quantitatively compensates for these two major sources of error. The method integrates finite element simulation and phantom experiments to establish correction functions for each influencing factor. These functions are then applied to USCT images from volunteers through automated parameter extraction and intensity adjustment. Validation on both phantom and in vivo data showed that the proposed method significantly improved the uniformity and diagnostic accuracy of echo intensity measurements, leading to a clear improvement in the diagnostic accuracy of MSK diseases. This method enhances the reliability of USCT-based quantitative diagnosis and holds strong potential for broader clinical adoption.