A New MRI-Defined Biomarker for Rectal Mucinous Adenocarcinoma: Mucin Pool Patterns in Determining the Efficacy of Neoadjuvant Therapy

Diagnostic value of dual-source, dual-energy computed tomography combined with the neutrophil-lymphocyte ratio for discriminating gastric signet ring cell from mixed signet ring cell and non-signet ring cell carcinomas

PURPOSE

To explore the diagnostic value of dual-source computed tomography (DSCT) and neutrophil to lymphocyte ratio (NLR) for differentiating gastric signet ring cell carcinoma (SRC) from mixed SRC (mSRC) and non-SRC (nSRC).

METHODS

This retrospective study included patients with gastric adenocarcinoma who underwent DSCT between August 2019 and June 2021 at our Hospital. The iodine concentration in the venous phase (ICvp), standardized iodine concentration (NICVP), and the slope of the energy spectrum curve (kVP) were extracted from DSCT data. NLR was determined from laboratory results. DSCT (including ICVP, NICVP, and kVP) and combination (including DSCT model and NLR) models were established based on the multinomial logistic regression analysis. The receiver operator characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic value.

RESULTS

A total of 155 patients (SRC [n = 45, aged 61.22 ± 11.4 years], mSRC [n = 60, aged 61.09 ± 12.7 years], and nSRC [n = 50, aged 67.66 ± 8.76 years]) were included. There were significant differences in NLR, ICVP, NICVP, and kVP among the SRC, mSRC, and nSRC groups (all P < 0.001). The AUC of the combination model for SRC vs. mSRC + nSRC was 0.964 (95% CI: 0.923-1.000), with a sensitivity of 98.3% and a specificity of 86.7%, higher than with DSCT (AUC: 0.959, 95% CI: 0.919-0.998, sensitivity: 90.0%, specificity: 89.9%) or NLR (AUC: 0.670, 95% CI: 0.577-0.768, sensitivity: 62.2%, specificity: 61.8%).

CONCLUSION

DSCT combined with NLR showed high diagnostic efficacy in differentiating SRC from mSRC and nSRC.

Mucinous adenocarcinoma: A unique clinicopathological subtype in colorectal cancer

Mucinous adenocarcinoma (MAC) is a unique clinicopathological subtype of colorectal cancer, which is characterized by extracellular mucinous components that comprise at least 50% of the tumor tissue. The clinical characteristics, molecular features, response to chemo-/radiotherapy, and prognosis of MAC are different from that of non-MAC (NMAC). MAC is more common in the proximal colon, with larger volume, higher T-stage, a higher proportion of positive lymph nodes, poorer tumor differentiation, and a higher proportion of peritoneal implants compared to NMAC. Although biopsy is the main diagnostic method for MAC, magnetic resonance imaging is superior in accuracy, especially for rectal carcinoma. The aberrant expression of mucins, including MUC1, MUC2 and MUC5AC, is a notable feature of MAC, which may be related to tumor invasion, metastasis, inhibition of apoptosis, and chemo-/radiotherapy resistance. The genetic origin of MAC is mainly related to BRAF mutation, microsatellite instability, and the CpG island methylator phenotype pathway. In addition, the poor prognosis of rectal MAC has been confirmed by various studies, and that of colonic MAC is still controversial. In this review, we summarize the epidemiology, clinicopathological characteristics, molecular features, methods of diagnosis, and treatments of MAC in order to provide references for further fundamental and clinical research.

Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram

Objective

To build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC).

Methods

Totally, 92 individuals with pathologically confirmed RMAC administered surgical resection upon nCRT in two different centers were assessed retrospectively (training set, n = 52, validation set, n = 40). Rectal MRI was performed pre-nCRT. Radiomics parameters were obtained from high-resolution T2-weighted images and selected to construct a radiomics signature. Then, radiomics nomogram construction integrated patient variables and the radiomics signature. The resulting radiomics nomogram was utilized to assess the tumor regression grade (TRG). Diagnostic performance was determined by generating receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

Results

Six optimal features related to TRG were obtained to construct a radiomics signature. The nomogram combining the radiomics signature with age and mucin deposit outperformed the radiomics signature alone in the training (AUC, 0.950 vs 0.843, p < 0.05) and validation (AUC, 0.868 vs 0.719, p < 0.05) cohorts. DCA demonstrated a clinical utility for the radiomics nomogram model.

Conclusions

The established quantitative MRI-based radiomics nomogram is effective in predicting treatment response to neoadjuvant therapy in patients with RMAC.