Prognostic value of multi b-value DWI in patients with locally advanced rectal cancer

Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction

Rectal cancer (RC) is one of the major health challenges worldwide. Accurate staging, restaging, invasiveness assessment, and treatment efficacy evaluation are crucial for its clinical management. Magnetic resonance imaging (MRI) plays a significant role in these processes. However, standard MRI techniques, including T2-weighted and diffusion-weighted imaging, have uncertainties in identifying early-stage tumors, high-risk nodules, extramural vascular invasion, and treatment efficacy, potentially leading to inappropriate treatment. Recent advances suggest that the integration of traditional MRI methods, including diffusion-weighted imaging, opposed-phase or contrast-enhanced T1-weighted imaging, as well as emerging synthetic MRI, could address these challenges. Additionally, improvements in imaging technology have spurred research into advanced functional MRI techniques such as diffusion kurtosis imaging and amide proton transfer weighted MRI, yielding promising results in RC assessment. Total neoadjuvant therapy has emerged as a new treatment paradigm for locally advanced RC, with neoadjuvant immunotherapy and chemotherapy offering viable alternatives to neoadjuvant chemoradiotherapy. However, the lack of standards for the early prediction of patient survival and tumor response to neoadjuvant therapy highlights a critical unmet need in matching therapies to suitable patients. Furthermore, organ preservation strategies after neoadjuvant therapy provide personalized options based on tumor response and patient preferences, yet traditional MRI assessments show significant variability. Radiomics and artificial intelligence hold promise for revealing complex patterns in MRI images associated with patient prognosis and treatment response. This review provides an overview of current MRI advancements in RC assessment and emphasizes how future research can refine tailored treatment strategies to improve patient outcomes. KEY POINTS: Question The accurate diagnosis of early-stage rectal tumors, high-risk nodules, treatment responses, and the early prediction of patient survival and therapeutic outcomes remain an unmet need. Findings Visual MRI has improved staging, restaging, and invasiveness evaluation. Advanced MRI, radiomics and artificial intelligence provide significant potential for tumor characterization and outcome prediction. Clinical relevance Advances in visual MRI are improving routine imaging protocols and radiomics and artificial intelligence show promise in enhancing treatment decisions through precise tumor characterization and outcome prediction.

Predicting disease-free survival in locally advanced rectal cancer using a prognostic model based on pretreatment b-value threshold map and postoperative pathologic features

PURPOSE

Disease-free survival (DFS) after neoadjuvant chemoradiotherapy (nCRT) is an important factor in affecting the quality of life and determining the subsequent treatment procedures for patients with locally advanced rectal cancer (LARC). This study aimed to develop a novel prognostic model for predicting the DFS in patients with LARC following nCRT and to verify its effectiveness.

MATERIALS AND METHODS

Patients with LARC who underwent magnetic resonance imaging (MRI) and nCRT at our institution between November 2017 and March 2022 were enrolled in this retrospective study. Clinicopathologic data and MRI indicators of all patients were collected and evaluated. All patients were divided into DFS and non-DFS groups according to the presence or absence of local recurrence or distant metastasis. The differences in the b-value threshold (bthreshold) and apparent diffusion coefficient (ADC) values between the DFS and non-DFS groups were compared. The Cox analyses were used to determine the risk factors in predicting the DFS. A merged model was established based on the risk factors, and a nomogram was constructed. The predictive performances of the merged model were validated using the receiver-operating characteristic (ROC) and decision curve analysis (DCA).

RESULTS

Of the 524 patients enrolled, 132 who underwent surgical resection post-nCRT were included in the final analysis. The post-neoadjuvant therapy pathological N stage, extramural venous invasion (EMVI), and bthreshold were independent factors in predicting the DFS. The C-index of the model was 0.688. The area under the curve (AUC) of the nomogram in predicting the 1-, 3-, and 5-year survival rates of patients was 0.731, 0.723, and 0.779, respectively. The DCA demonstrated that the merged model had a greater advantage than either the "all" or the "none" scheme when the threshold probability was between 0.1 and 0.65.

CONCLUSION

A merged model based on the bthreshold value and clinicopathological features showed the potential to predict the prognosis of patients with LARC after nCRT and surgery.

Predictive potential of MRI in differentiating the predominant component in biphasic pleural mesothelioma

PURPOSE

To assess the potential of apparent diffusion coefficient (ADC) values derived from diffusion weighted (DW) MRI preoperatively to predict the predominant histologic component among biphasic pleural mesothelioma (PM) tumors.

METHODS

ADC maps were generated from DW MRI scans. Histology and predominant component of biphasic PM were confirmed following surgical resection. Statistical analyses were done with R (R Foundation for Statistical Computing, Vienna, Austria). Average ADC values corresponding to epithelioid- and sarcomatoid-predominant tumors were compared. ADC thresholding was accomplished by recursive partitioning and confirmed with ROC analysis.

RESULTS

Eighty-four patients with biphasic PM's, 69 (82 %) epithelioid-predominant (BE) and 15(18 %) sarcomatoid-predominant (BS) tumors were evaluated. Thirty-eight (45 %) patients underwent extrapleural pneumonectomy (EPP), 39 (46 %) had extended pleural decortication (ePDC) and 7 (8 %) had pleural decortication (PDC). ADC values ranged between 0.696 x 10-3 to 1.921 x 10-3 mm2/s. BE tumors demonstrated significantly higher ADC values than BS tumors (p = 0.026). ADC values above 0.94 x 10-3 mm2/s were associated with a significant increase of relative risk of being in group BE over group BS (relative risk: 1.47, 95 %CI: 1.05-2.06, p = 0.027) CONCLUSION: Average ADC values of BE tumors were higher than BS tumors and the two groups can be separated by a cut off value of 0.94 X 10-3 mm2/s.

Utility of clinical and MR imaging parameters for prediction and monitoring of response to capecitabine and temozolomide (CAPTEM) therapy in patients with liver metastases of neuroendocrine tumors

BACKGROUND

This study explores the predictive and monitoring capabilities of clinical and multiparametric MR parameters in assessing capecitabine and temozolomide (CAPTEM) therapy response in patients with neuroendocrine tumors (NET).

PATIENTS AND METHODS

This retrospective study (n = 44) assessed CAPTEM therapy response in neuroendocrine liver metastases (NELM) patients. Among 33 monitored patients, as a subgroup of the overall study cohort, pretherapeutic and follow-up MRI data (size, apparent diffusion coefficient [ADC] values, and signal intensities), along with clinical parameters (chromogranin A [CgA] and Ki-67%), were analyzed. Progression-free survival (PFS) served as the reference. Responders were defined as those with PFS ≥ 6 months.

RESULTS

Most patients were male (75%) and had G2 tumors (76%) with a pancreatic origin (84%). Median PFS was 5.7 months; Overall Survival (OS) was 25 months. Non-responders (NR) had higher Ki-67 in primary tumors (16.5 vs. 10%, p = 0.01) and increased hepatic burden (20% vs. 5%, p = 0.007). NR showed elevated CgA post-treatment, while responders (R) exhibited a mild decrease. ADC changes differed significantly between groups, with NR having decreased ADCmin (-23%) and liver-adjusted ADCmean/ADCmean liver (-16%), compared to R's increases of ADCmin (50%) and ADCmean/ADCmean liver (30%). Receiver operating characteristic (ROC) analysis identified the highest area under the curve (AUC) (0.76) for a single parameter for ∆ ADC mean/liver ADCmean, with a cut-off of < 6.9 (76% sensitivity, 75% specificity). Combining ∆ Size NELM and ∆ ADCmin achieved the best balance (88% sensitivity, 60% specificity) outperforming ∆ Size NELM alone (69% sensitivity, 65% specificity). Kaplan-Meier analysis indicated significantly longer PFS for ∆ ADCmean/ADCmean liver < 6.9 (p = 0.024) and ∆ Size NELM > 0% + ∆ ADCmin < -2.9% (p = 0.021).

CONCLUSIONS

Survival analysis emphasizes the need for adapted response criteria, involving combined evaluation of CgA, ADC values, and tumor size for monitoring CAPTEM response in hepatic metastasized NETs.

Quantitative analysis of contrast-enhanced ultrasound in neoadjuvant treatment of locally advanced rectal cancer: a retrospective study

Purpose

To explore the clinical value of contrast-enhanced ultrasound (CEUS) quantitative analysis in the evaluation and prognosis of neoadjuvant chemoradiotherapy for locally advanced rectal cancer (LARC).

Methods

Eighty-three consecutive patients undergoing neoadjuvant chemoradiotherapy and total mesorectal excision for LARC were retrospectively included. According to pathological results, patients were categorized into complete or incomplete response groups. Differences in ultrasonic parameters, pathological results, and clinical data between groups were evaluated. The cutoff point for a complete response as determined by quantitative analysis of CEUS was assessed using a receiver operating characteristic curve; additionally, overall survival (OS) and progression-free survival (PFS) were analyzed.

Results

Of the 83 patients, 12 (14.5%) achieved a complete response and 71 (85.5%) did not. There were significant between-group differences in carcinoembryonic antigen (CEA) levels, differentiation degree, proportion of tumor occupying the lumen, anterior-posterior and superior-inferior diameters of the lesion, and intensity of enhancement (P<0.05). CEUS quantitative analysis showed significant between-group differences in peak intensity (PI) and area under the curve (AUC) values (P<0.05). The OS and PFS of patients with high PI, high AUC value, and poorly differentiated cancer were significantly worse than those with low PI, low AUC values, and moderately to highly differentiated cancer (P<0.05). High CEA levels (hazard ratio: 1.02, 95% confidence interval: 1.01-1.04; P=0.002) and low differentiation (2.72, 1.12-6.62; P=0.028) were independent risk factors for PFS and OS.

Conclusions

CEUS can predict the response to neoadjuvant treatment in patients with LARC. CEUS quantitative analysis is helpful for clinical prognosis.