Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer

Predicting tumor invasion depth in gastric cancer: developing and validating multivariate models incorporating preoperative IVIM-DWI parameters and MRI morphological characteristics

INTRODUCTION

Accurate assessment of the depth of tumor invasion in gastric cancer (GC) is vital for the selection of suitable patients for neoadjuvant chemotherapy (NAC). Current problem is that preoperative differentiation between T1-2 and T3-4 stage cases in GC is always highly challenging for radiologists.

METHODS

A total of 129 GC patients were divided into training (91 cases) and validation (38 cases) cohorts. Pathology from surgical specimens categorized patients into T1-2 and T3-4 stages. IVIM-DWI and MRI morphological characteristics were evaluated, and a multimodal nomogram was developed. The MRI morphological model, IVIM-DWI model, and combined model were constructed using logistic regression. Their effectiveness was assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

RESULTS

The combined nomogram, integrating preoperative IVIM-DWI parameters (D value) and MRI morphological characteristics (maximum tumor thickness, extra-serosal invasion), achieved the highest area under the curve (AUC) values of 0.901 and 0.883 in the training and validation cohorts, respectively. No significant difference was observed between the AUCs of the IVIM-DWI and MRI morphological models in either cohort (training: 0.796 vs. 0.835, p = 0.593; validation: 0.794 vs. 0.766, p = 0.79).

CONCLUSION

The multimodal nomogram, combining IVIM-DWI parameters and MRI morphological characteristics, emerges as a promising tool for assessing tumor invasion depth in GC, potentially guiding the selection of suitable candidates for neoadjuvant chemotherapy (NAC) treatment.

Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a prevalent cancer type in clinical settings; its early signs can be difficult to detect, which often results in late-stage diagnoses in many patients. The early detection and diagnosis of CRC are crucial for improving treatment success and patient survival rates. Recently, imaging techniques have been hypothesized to be essential in managing CRC, with magnetic resonance imaging (MRI) and spiral computed tomography (SCT) playing a significant role in enhancing diagnostic and treatment approaches.

AIM

To explore the effectiveness of MRI and SCT in the preoperative staging of CRC and the prognosis of laparoscopic treatment.

METHODS

Ninety-five individuals admitted to Zhongshan Hospital Xiamen University underwent MRI and SCT and were diagnosed with CRC. The precision of MRI and SCT for the presurgical classification of CRC was assessed, and pathological staging was used as a reference. Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of blood volume, blood flow, time to peak, permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction on the prognosis of patients with CRC.

RESULTS

Pathological biopsies confirmed the following CRC stages: 23, 23, 32, and 17 at T1, T2, T3, and T4, respectively. There were 39 cases at the N0 stage, 22 at N1, 34 at N2, 44 at M0 stage, and 51 at M1. Using pathological findings as the benchmark, the combined use of MRI and SCT for preoperative TNM staging in patients with CRC demonstrated superior sensitivity, specificity, and accuracy compared with either modality alone, with a statistically significant difference in accuracy (P < 0.05). Receiver operating characteristic curve analysis revealed the predictive values for laparoscopic treatment prognosis, as indicated by the areas under the curve for blood volume, blood flow, time to peak, and permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction were 0.750, 0.683, 0.772, 0.761, 0.709, 0.719, and 0.910, respectively. The corresponding sensitivity and specificity values were also obtained (P < 0.05).

CONCLUSION

MRI with SCT is effective in the clinical diagnosis of patients with CRC and is worthy of clinical promotion.

Construction and validation of the prognostic nomogram model for patients with diffuse-type gastric cancer based on the SEER database

OBJECTIVE

The prognostic factors of diffuse GC patients were screened the prognostic nomogram was constructed, and the prediction accuracy was verified.

METHODS

From 2006 to 2018, there were 2877 individuals pathologically diagnosed with diffuse gastric cancer; the clinicopathological features of these patients were obtained from the SEER database & randomly divided into a training cohort (1439) & validation cohort (1438).To create prognostic nomograms & choose independent prognostic indicators to predict the overall survival (OS) of 1, 3, & 5 years, log-rank & multivariate COX analysis were utilized & discrimination ability of nomogram prediction using consistency index and calibration curve.

RESULTS

Age, T, N, M, TNM, surgical status, chemotherapy status, & all seven markers were independent predictors of OS (P < 0.05), & a nomogram of OS at 1, 3, & 5 years was created using these independent predictors. The nomogram's c-index was 0.750 (95% CI 0.734 ~ 0.766), greater than the TNM staging framework 0.658 (95%CI 0.639 ~ 0.677); the c-index was 0.753 (95% CI 0.737 ~ 0.769) as well as superior to the TNM staging mechanism 0.679 (95% CI 0.503-0.697). According to the calibration curve, the projected survival rate using the nomogram & the actual survival rate are in good agreement.

CONCLUSIONS

Prognostic nomograms are useful tools for physicians to assess every individual's individualised prognosis & create treatment strategies for those with diffuse gastric cancer. They can reliably predict the prognosis for individuals with diffuse gastrointestinal carcinoma.

Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy

BACKGROUND

There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC.

METHODS

Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model.

RESULTS

ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively.

CONCLUSIONS

Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.

Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy

Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.

Application of intravoxel incoherent motion diffusion-weighted imaging for preoperative knowledge of lymphovascular invasion in gastric cancer: a prospective study

PURPOSE

To investigate the potential of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer (GC).

METHODS

This study prospectively enrolled 90 patients (62 males, 28 females, 60.79 ± 9.99 years old) who received radical gastrostomy. Abdominal MRI examinations including IVIM were performed within 1 week before surgery. Patients were divided into LVI-positive and -negative group according to pathological diagnosis after surgery. The apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f), were compared between the two groups. The relationship between MRI parameters and LVI was studied by Spearman's correlation analysis. Multivariable logistic regression analysis was used to screen independent predictors of LVI. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy.

RESULTS

The ADC, D in LVI-positive group were lower, whereas tumor thickness and f parameter in LVI-positive group were higher than those in LVI-negative group, and they were statistically correlated with LVI (p < 0.05). D, f and tumor thickness were independent risk factors of LVI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D + f + thickness) were 0.667, 0.754, 0.695, 0.792, and 0.876, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p < 0.05).

CONCLUSION

The ADC, D, and f can effectively distinguish LVI status of GC. The D, f and thickness were independent predictors. The combination of the three predictors further improved the efficacy.

Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features

PURPOSE

To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment.

MATERIALS AND METHODS

The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n = 120) and validation set (n = 60) and are categorized into effective group (n = 83) and ineffective group (n = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model.

RESULTS

Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model.

CONCLUSION

CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment.

Neoadjuvant Chemotherapy in Asian Patients With Locally Advanced Gastric Cancer

Presently, surgery is the only treatment approach for gastric cancer and improving the prognosis of locally advanced gastric cancer is one of the key factors in promoting gastric cancer survival benefit. The MAGIC study was the first to demonstrate the efficacy of neoadjuvant chemotherapy (NAC) in European countries. In recent years, several clinical trials have provided evidence for the use of NAC in Asian patients with locally advanced gastric cancer. However, clinical practice guidelines vary between Asian and non-Asian populations. Optimal NAC regimens, proper target populations, and predictors of NAC outcomes in Asian patients are still under investigation. Herein, we summarized the current progress in the administration of NAC in Asian patients with gastric cancer.