Diffusion-weighted MR volume and apparent diffusion coefficient for discriminating lymph node metastases and good response after chemoradiation therapy in locally advanced rectal cancer

T1 mapping in evaluation of clinicopathologic factors for rectal adenocarcinoma

OBJECTIVE

T1 mapping has been increasingly applied in the study of tumor. The purpose of this study was to evaluate the value of T1 mapping in evaluating clinicopathologic factors for rectal adenocarcinoma.

MATERIALS AND METHODS

Eighty-six patients with rectal adenocarcinoma confirmed by surgical pathology who underwent preoperative pelvic MRI were retrospectively analyzed. High-resolution T2-weighted imaging (T2WI), T1 mapping, and diffusion-weighted imaging (DWI) were performed. T1 and apparent diffusion coefficient (ADC) parameters were compared among different associated tumor markers, tumor grades, stages, and structure invasion statuses. A receiver operating characteristic (ROC) analysis was estimated.

RESULTS

T1 value showed significant difference between high- and low-grade tumors ([1531.5 ± 84.7 ms] vs. [1437.1 ± 80.3 ms], P < 0.001). T1 value was significant higher in positive than in negative perineural invasion ([1495.7 ± 89.2 ms] vs. [1449.4 ± 88.8 ms], P < 0.05). No significant difference of T1 or ADC was observed in different CEA, CA199, T stage, N stage, lymphovascular invasions, extramural vascular invasion (EMVI), and circumferential resection margin (CRM) (P > 0.05). The AUC under ROC curve of T1 value were 0.796 in distinguishing high- from low-grade rectal adenocarcinoma. The AUC of T1 value in distinguishing perineural invasion was 0.637.

CONCLUSION

T1 value was helpful in assessing pathologic grade and perineural invasion correlated with rectal cancer.

Role of apparent diffusion coefficient in assessment of loco-regional nodal spread in cancer rectum: correlative study with histopathological findings

Background

Rectal cancer is associated with high morbidity and mortality rates. Preoperative assessment and detection of nodal metastasis are crucial for selecting a proper treatment plan. Diffusion-weighted imaging is considered to be a crucial functional imaging technique that can aid in determining the condition of lymph nodes. This study aimed to assess the diagnostic utility of MRI functional images by use of apparent diffusion coefficient in regional nodal assessment in rectal cancer.

Results

This study included 54 patients including 29 males (53.7%) and 25 females (46.3%) presented with pathologically proven rectal cancer. Regarding rectal adenocarcinoma, functional MRI imaging using ADC values found to have a better sensitivity (86.24%) in detection of regional nodal metastasis than conventional morphological MRI criteria with 1.05 × 10−3 mm2/s was employed as cutoff value to distinguish metastatic from non-metastatic lymph nodes with statistically significant P value (< 0.001); nevertheless, regarding the accuracy there was no difference (68.52%). As regards mucinous and signet ring cell carcinoma, morphological assessment using conventional MRI sequences were found to have a better accuracy (72.96%) and sensitivity (57.69%) than ADC value, with the latter showed low statistically significant results (P- value < 0.201) in distinguishing metastatic and non-metastatic nodes. This could be explained by extremely high ADC values of nodes for these pathological types owing to their high mucin content.

Conclusions

MRI functional imaging using ADC values can be utilized to distinguish metastatic from non-metastatic lymph nodes in rectal adenocarcinoma employing diagnostic accuracy of 86.52%. However, morphological assessment using conventional MRI was found to be better in assessment of regional lymph nodes at mucinous and signet ring rectal carcinoma.

Prediction of hepatic lymph node metastases based on magnetic resonance imaging before and after preoperative chemotherapy in patients with colorectal liver metastases underwent surgical resection

BACKGROUND

Patients with colorectal liver metastases (CRLM) combined with hepatic lymph node (HLN) metastases have a poor prognosis. In this study, we developed and validated a model using clinical and magnetic resonance imaging (MRI) parameters to predict HLN status before surgery.

METHODS

A total of 104 CRLM patients undergoing hepatic lymphonodectomy with pathologically confirmed HLN status after preoperative chemotherapy were enrolled in this study. The patients were further divided into a training group (n = 52) and a validation group (n = 52). The apparent diffusion coefficient (ADC) values, including ADC_mean and ADC_min of the largest HLN before and after treatment, were measured. rADC was calculated referring to the target liver metastases, spleen, and psoas major muscle (rADC_-LM, rADC_-SP, rADC_-m). In addition, ADC change rate (Δ% ADC) was quantitatively calculated. A multivariate logistic regression model for predicting HLN status in CRLM patients was constructed using the training group and further tested in the validation group.

RESULTS

In the training cohort, post-ADC_mean (P = 0.018) and the short diameter of the largest lymph node after treatment (P = 0.001) were independent predictors for metastatic HLN in CRLM patients. The model's AUC was 0.859 (95% CI, 0.757-0.961) and 0.767 (95% CI 0.634-0.900) in the training and validation cohorts, respectively. Patients with metastatic HLN showed significantly worse overall survival (p = 0.035) and recurrence-free survival (p = 0.015) than patients with negative HLN.

CONCLUSIONS

The developed model using MRI parameters could accurately predict HLN metastases in CRLM patients and could be used to preoperatively assess the HLN status and facilitate surgical treatment decisions in patients with CRLM.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Objectives

To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).

Methods

Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).

Results

There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.

Conclusion

MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

Native T1 mapping for differentiating the histopathologic type, grade, and stage of rectal adenocarcinoma: a pilot study

Background

Previous studies have indicated that T1 relaxation time could be utilized for the analysis of tissue characteristics. T1 mapping technology has been gradually used on research of body tumor. In this study, the application of native T1 relaxation time for differentiating the histopathologic type, grade, and stage of rectal adenocarcinoma was investigated.

Methods

One hundred and twenty patients with pathologically confirmed rectal adenocarcinoma were retrospectively evaluated. All patients underwent high-resolution anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and T1 mapping sequences. Parameters of T1 relaxation time and apparent diffusion coefficient (ADC) were measured between the different groups. The diagnostic power was evaluated though the receiver operating characteristic (ROC) curve.

Results

The T1 and ADC values varied significantly between rectal mucinous adenocarcinoma (MC) and non-mucinous rectal adenocarcinoma (AC) ([1986.1 ± 163.3 ms] vs. [1562.3 ± 244.2 ms] and [1.38 ± 0.23 × 10⁻³mm²/s] vs. [1.03 ± 0.15 × 10⁻³mm²/s], respectively; P < 0.001). In the AC group, T1 relaxation time were significantly different between the low- and high-grade adenocarcinoma cases ([1508.7 ± 188.6 ms] vs. [1806.5 ± 317.5 ms], P < 0.001), while no differences were apparent in the ADC values ([1.03 ± 0.14 × 10⁻³mm²/s] vs. [1.04 ± 0.18 × 10⁻³mm²/s], P > 0.05). No significant differences in T1 and ADC values were identified between the different T and N stage groups for both MC and AC (all P > 0.05).

Conclusions

Native T1 relaxation time can be used to discriminate MC from AC. The T1 relaxation time was helpful for differentiating the low- and high-grade of AC.

Role of MRI‑based radiomics in locally advanced rectal cancer (Review)

Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.