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

Application of T1-mapping combined with high-spatial-resolution T2-weighted imaging in discriminating mucinous from nonmucinous adenocarcinoma in rectal cancer

PURPOSE

The aim of this study was to evaluate the utility of T1-mapping, high-spatial-resolution T2-weighted imaging (HR-T2WI), and their combined model in distinguishing between adenocarcinoma not otherwise specified (AC) and mucinous adenocarcinoma (MC) in rectal cancer.

METHODS

A total of 55 patients with pathologically confirmed AC and 37 patients with MC were included in the study. Two radiologists independently reviewed the HR-T2WI and provided assessments of histopathological type. Additionally, T1 relaxation times were quantified using whole-tumor volume methods both pre-contrast (T1p) and post-contrast administration (T1e). The absolute reduction in T1 value (T1d) and the percentage reduction (T1d%) were calculated. Receiver operating characteristic curve analysis was performed to evaluate diagnostic efficacy.

RESULTS

HR-T2WI demonstrated a sensitivity, specificity, and accuracy of 81.08%, 94.55%, and 89.13%, respectively, in distinguishing rectal MC. T1p, T1e, and T1d values were significantly higher in the MC group compared to the AC group (P < 0.001, = 0.019, and < 0.001, respectively), while there was no statistically significant difference in T1d% between the two groups. Among these quantitative parameters, T1p showed the highest diagnostic efficiency for identifying MC, with a sensitivity of 59.46%, specificity of 92.73%, and moderate diagnostic accuracy (AUC = 0.819). Combining HR-T2WI with T1p (sensitivity = 86.49%, specificity = 92.73, AUC = 0.927) yielded superior performance over single parameters in distinguishing histopathological subtypes.

CONCLUSION

T1p is effective in discriminating between AC and MC in rectal cancer. Importantly, the combined model incorporating HR-T2WI and T1p demonstrated enhanced capability in distinguishing histopathological subtypes of rectal cancer, which benefits individualized treatment.

Application of cellular microstructural diffusion MRI (cell size imaging) in rectal lesions: a preliminary study

Objectives

To explore the value of cellular microstructural mapping by IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) method in evaluating the histological type and prognostic factors of rectal lesions.

Materials and methods

Sixty-six patients with rectal lesions were enrolled in this study. All subjects underwent MRI scans including conventional diffusion weighted imaging (DWI) and the IMPULSED MRI scans of oscillating gradient spin-echo (OGSE) and pulse gradient spin-echo (PGSE) sequences. Parameters including mean cell diameter (dmean), intracellular fraction (vin), extracellular diffusivity (dex), cellularity, and apparent diffusion coefficient (ADC) values (ADCPGSE, ADC17Hz, ADC33Hz, and ADC of conventional DWI) were measured in different histopathologic types, grades, stages, and structure invasion statuses. The receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic power. The sensitivity, specificity, and the corresponding area under the curves (AUCs) were calculated.

Results

Our preliminary results illustrated that malignant lesion showed higher vin and cellularity ([0.2867 ± 0.0697] vs. [0.1856 ± 0.1011], [2.3508 ± 0.6055] vs. [1.2716 ± 0.4574], all P<0.05), lower dex and ADC values (ADCPGSE, ADC17Hz, and ADC of conventional DWI) compared to benign lesion ([2.1637 ± 0.3303 μm2/ms] vs. [2.5595 ± 0.5085 μm2/ms], [0.9238 (0.7959, 1.0741) ×10-3 mm2/s] vs. [1.3373 ± 0.3902×10-3 mm2/s], [1.3204 ± 0.2342×10-3 mm2/s] vs. [1.8029 ± 0.3119×10-3 mm2/s], [0.7400 (0.6750, 0.8375) ×10-3 mm2/s] vs. [1.0550 ± 1.1191×10-3 mm2/s], all P<0.05), while no significant difference was seen for dmean. Vin and cellularity of rectal common adenocarcinoma (AC) were significantly higher than those of rectal mucinous adenocarcinoma (MC) ([0.2994 ± 0.0626] vs. [0.2028 ± 0.0571], [2.4579 ± 0.5553] vs. [1.6412 ± 0.4347], all P<0.05), while dex and ADC values (ADCPGSE, ADC17Hz, ADC33Hz, and ADC of conventional DWI) were lower in AC ([2.1189 ± 0.3187 μm2/ms] vs. [2.4609 ± 0.2534 μm2/ms], [0.8996 ± 0.1583×10-3 mm2/s] vs. [1.2072 ± 0.2326×10-3 mm2/s], [1.2714 ± 0.1916×10-3 mm2/s] vs. [1.6451 ± 0.2420×10-3 mm2/s], [1.8963 (1.6481, 2.1138) ×10-3 mm2/s] vs. [2.3104 ± 0.3851×10-3 mm2/s], [0.7341 ± 0.8872×10-3 mm2/s] vs. [1.1410 ± 0.1840×10-3 mm2/s], all P<0.05). In AC group, the dmean had significant difference between negative and positive tumor budding (TB) ([13.2590 ± 1.3255 μm] vs. [14.3014 ± 1.1830 μm], P<0.05). No significant difference of dmean, vin, dex, cellularity or ADC values was observed in AC with different grade, T stage, N stage, perineural and lymphovascular invasion (all P>0.05). The ROC curves showed that the area under the curves (AUCs) of vin, dex, cellularity, and ADC values (ADCPGSE, ADC17Hz, and ADC of conventional DWI) for distinguishing malignant and benign lesion were 0.803, 0.757, 0.948, 0.807, 0.908 and 0.905, respectively. The AUCs of vin, dex, cellularity, and ADC values (ADCPGSE, ADC17Hz, ADC33Hz, and ADC of conventional DWI) in distinguishing AC from MC were 0.887, 0.802, 0.906, 0.896, 0.896, 0.781 and 0.991, respectively. The AUC of the dmean for evaluating TB status was 0.726. The AUC of ADC from conventional DWI for evaluating WHO grade was 0.739.

Conclusion

Cellular microstructural mapping by the IMPULSED method has great potential in preoperative evaluation of rectal lesions. It could be helpful in differentiating malignant and benign lesions, distinguishing AC from MC, and in predicting the TB status.

Prediction of mucinous adenocarcinoma in colorectal cancer with mucinous components detected in preoperative biopsy diagnosis

OBJECTIVES

Endoscopic biopsy diagnosis for the preoperative assessment of mucinous components in patients with colorectal cancer is limited. This study investigated a radiomics model and established an explainable prediction model by using machine learning to differentiate between adenocarcinoma with mucinous components and mucinous adenocarcinoma.

METHODS

The derivation cohort included 312 patients with colorectal cancer with mucinous components detected during preoperative endoscopic biopsy diagnosis. These patients were randomly divided into training and validation sets in a 7:3 ratio. Radiomics features were extracted, followed by feature engineering, to create a radiomic score (radscore). Subsequently, 24 features, including the radscore, clinical data, and serological characteristics, were used to develop machine learning models by using nine different machine learning algorithms. The SHapley Additive exPlanation (SHAP) method was employed to elucidate the workings of the machine learning models and visualize individual variable predictions.

RESULTS

The radiomics model achieved an area under the curve (AUC) of 0.810. The random forest model outperformed the other models and had the highest AUC of 0.832; thus, this model was defined as the hybrid model. The clinical model, which was built using clinical data and serological characteristics, had an AUC of 0.732, whereas the radiomics model achieved an AUC of 0.810. SHAP model interpretation revealed that among the 14 features with non-zero SHAP values, the radscore and clinical T stage had notably higher values.

CONCLUSION

This interpretable predictive model effectively differentiates between adenocarcinoma with mucinous components and mucinous adenocarcinoma in patients with colorectal cancer, thereby facilitating informed treatment decisions for individuals in whom mucinous components are identified during preoperative biopsy diagnosis.

An Exploratory Study: Can Native T1 Mapping Differentiate Sarcoma from Benign Soft Tissue Tumors at 1.5 T and 3 T?

Background/Objectives: T1 relaxation time has been shown to be valuable in detecting and characterizing tumors in various organs. This study aims to determine whether native T1 relaxation time can serve as a useful tool in distinguishing sarcomas from benign tumors. Methods: In this retrospective study, patients with histologically confirmed soft tissue sarcomas and benign tumors were included. Only patients who had not undergone prior treatment or surgery and whose magnetic resonance imaging (MRI) included native T1 mapping were considered. Images were acquired using both 1.5 T and 3 T MRI scanners. T1 histogram parameters were measured in regions of interest encompassing the entire tumor volume, as well as in healthy muscle tissue. Results: Out of 316 cases, 16 sarcoma cases and 9 benign tumor cases were eligible. The T1 values observed in sarcoma did not significantly differ from those in benign lesions in both 1.5 T and 3 T MRIs (p1.5T = 0.260 and p3T = 0.119). However, T1 values were found to be lower in healthy tissues compared to sarcoma at 3 T (p = 0.020), although this difference did not reach statistical significance at 1.5 T (p = 0.063). At both 1.5 T and 3 T, no significant difference between healthy muscle measured in sarcoma cases or benign tumor cases was observed (p1.5T = 0.472 and p3T = 0.226). Conclusions: T1 mapping has the potential to serve as a promising tool for differentiating sarcomas from benign tumors in baseline assessments. However, the standardization of imaging protocols and further improvements in T1 mapping techniques are necessary to fully realize its potential.

Glucose chemical exchange saturation transfer MRI for predicting the histological grade of rectal cancer: a comparative study with amide proton transfer-weighted and diffusion-weighted imaging

BACKGROUND

To evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer.

METHODS

This prospective analysis included 60 patients with preoperative rectal cancer who underwent pelvic glucoCEST, amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI). In total, 21 low-grade and 39 high-grade cases were confirmed by postoperative pathology. The MTRasym (1.2 ppm), MTRasym (3.5 ppm), and apparent diffusion coefficient (ADC) values of lesions between the low-grade and high-grade groups were compared. The area under the receiver operating characteristic curve (AUC) was generated to evaluate the diagnostic performance of each technique. Logistic regression (LR) analysis was applied to determine independent predictors and for multi-parameter combined diagnosis.

RESULTS

Elevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all p < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC for differentiating between low- and high-grade rectal cancer cases were 0.792, 0.839, and 0.855, respectively. The diagnostic performance of the combination of the three indexes was improved (AUC, 0.969; sensitivity, 95.24%; specificity, 87.18%). The good consistency and reliability of the combination of independent predictors were demonstrated by calibration curve analysis and DCA.

CONCLUSION

The glucoCEST MRI without contrast injection, APTWI, and DWI all facilitate the assessment of histological grade in rectal cancer, and the combination of the three can effectively discriminate between high- and low-grade rectal cancer, which is expected to be a promising imaging marker.

CRITICAL RELEVANCE STATEMENT

The glucose chemical exchange saturation transfer MRI method facilitates the assessment of histological grade in rectal cancer and offers additional information to improve the diagnostic performance of amide proton transfer-weighted imaging, and diffusion-weighted imaging.

KEY POINTS

Glucose chemical exchange saturation transfer imaging could differentiate histological grade. Amide proton transfer-weighted and diffusion-weighted were associated with histological grade. The combination of different parameters showed the best diagnostic performance.

The diagnostic performance of T1 mapping in the assessment of breast lesions: A preliminary study

PURPOSE

To assess T1 mapping performance in distinguishing between benign and malignant breast lesions and to explore its correlation with histopathologic features in breast cancer.

METHODS

This study prospectively enrolled 103 participants with a total of 108 lesions, including 25 benign and 83 malignant lesions. T1 mapping, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) were performed. Two radiologists independently outlined the ROIs and analyzed T1 and apparent diffusion coefficient (ADC) values for each lesion, assessing interobserver reliability with the intraclass correlation coefficient (ICC). T1 and ADC values were compared between benign and malignant lesions, across different histopathological characteristics (histological grades, estrogen, progesterone and HER2 receptors expression, Ki67, N status). Receiver operating characteristic (ROC) analysis and Pearson correlation coefficient (ρ) were performed.

RESULTS

T1 values showed statistically significant differences between benign and malignant groups (P < 0.001), with higher values in the malignant (1817.08 ms ± 126.64) compared to the benign group (1429.31 ms ± 167.66). In addition, T1 values significantly increased in the ER (-) group (P = 0.001). No significant differences were found in T1 values among HER2, Ki67, N status, and histological grades groups. Furthermore, T1 values exhibited a significant correlation (ρ) with ER (P < 0.01) and PR (P = 0.03). The AUC for T1 value in distinguishing benign from malignant lesions was 0.69 (95 % CI: 0.55 - 0.82, P = 0.005), and for evaluating ER status, it was 0.75 (95 % CI: 0.62 - 0.87, P = 0.002).

CONCLUSIONS

T1 mapping holds the potential as an imaging biomarker to assist in the discrimination of benign and malignant breast lesions and assessing the ER expression status in breast cancer.

Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer

Background

In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response.

Methods

This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter.

Results

Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively].

Conclusions

Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

[Staging and Diagnostics of Rectal Cancer]

The review titled "Staging and Diagnostics of Rectal Cancer" aims to provide insight to imaging techniques in patients with rectal cancer.Rectal cancer is among the most common malignancies, with one of the highest mortality rates worldwide. Timely diagnosis and therapy of this cancer therefore has important socio-economic implications.Radiological imaging plays a major role in the planning of subsequent therapy. Modern tomographic imaging is used not only for initial diagnosis, but also for staging.The individual role of different imaging techniques in diagnosis of rectal cancer will be explained in detail, and their function in general. Furthermore, we will present relevant radiological research related.The increasing role of MRI-based local staging will be presented in detail in this review. Defined diagnostic criteria, based on common recommendations, will be explained. We will show how MRI-based local staging can support the initial diagnosis and follow-up examinations in collaboration with other medical specialties in therapeutic planning. In particular, we describe how MRI is capable of substantially influencing the determination of surgical procedures in rectal cancer.

T1 mapping as a quantitative imaging biomarker for diagnosing cervical cancer: a comparison with diffusion kurtosis imaging

BACKGROUND

T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. This study was conducted to explore the ability of T1 mapping in distinguishing cervical cancer type, grade, and stage and compare the diagnostic performance of T1 mapping with diffusion kurtosis imaging (DKI).

METHODS

One hundred fifty-seven patients with pathologically confirmed cervical cancer were enrolled in this prospectively study. T1 mapping and DKI were performed. The native T1, difference between native and postcontrast T1 (T1diff), mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were calculated. Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAC), low- and high-grade carcinomas, and early- and advanced-stage groups were compared using area under the receiver operating characteristic (AUROC) curves.

RESULTS

The native T1 and MK were higher, and the MD and ADC were lower for CSCC than for CAC (all p < 0.05). Compared with low-grade CSCC, high-grade CSCC had decreased T1diff, MD, ADC, and increased MK (p < 0.05). Compared with low-grade CAC, high-grade CAC had decreased T1diff and increased MK (p < 0.05). Native T1 was significantly higher in the advanced-stage group than in the early-stage group (p < 0.05). The AUROC curves of native T1, MK, ADC and MD were 0,772, 0.731, 0.715, and 0.627, respectively, for distinguishing CSCC from CAC. The AUROC values were 0.762 between high- and low-grade CSCC and 0.835 between high- and low-grade CAC, with T1diff and MK showing the best discriminative values, respectively. For distinguishing between advanced-stage and early-stage cervical cancer, only the AUROC of native T1 was statistically significant (AUROC = 0.651, p = 0.002).

CONCLUSIONS

Compared with DKI-derived parameters, native T1 exhibits better efficacy for identifying cervical cancer subtype and stage, and T1diff exhibits comparable discriminative value for cervical cancer grade.

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.

T1 and T2 mapping for identifying malignant lymph nodes in head and neck squamous cell carcinoma

BACKGROUND

This study seeks to assess the utility of T1 and T2 mapping in distinguishing metastatic lymph nodes from reactive lymphadenopathy in patients with head and neck squamous cell carcinoma (HNSCC), using diffusion-weighted imaging (DWI) as a comparison.

METHODS

Between July 2017 and November 2019, 46 HNSCC patients underwent neck MRI inclusive of T1 and T2 mapping and DWI. Quantitative measurements derived from preoperative T1 and T2 mapping and DWI of metastatic and non-metastatic lymph nodes were compared using independent samples t-test or Mann-Whitney U test. Receiver operating characteristic curves and the DeLong test were employed to determine the most effective diagnostic methodology.

RESULTS

We examined a total of 122 lymph nodes, 45 (36.9%) of which were metastatic proven by pathology. Mean T2 values for metastatic lymph nodes were significantly lower than those for benign lymph nodes (p < 0.001). Conversely, metastatic lymph nodes exhibited significantly higher apparent diffusion coefficient (ADC) and standard deviation of T1 values (T1SD) (p < 0.001). T2 generated a significantly higher area under the curve (AUC) of 0.890 (0.826-0.954) compared to T1SD (0.711 [0.613-0.809]) and ADC (0.660 [0.562-0.758]) (p = 0.007 and p < 0.001). Combining T2, T1SD, ADC, and lymph node size achieved an AUC of 0.929 (0.875-0.983), which did not significantly enhance diagnostic performance over using T2 alone (p = 0.089).

CONCLUSIONS

The application of T1 and T2 mapping is feasible in differentiating metastatic from non-metastatic lymph nodes in HNSCC and can improve diagnostic efficacy compared to DWI.

Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response

BACKGROUND

To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups.

METHODS

Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated.

RESULTS

Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71).

CONCLUSIONS

The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

CRITICAL RELEVANCE STATEMENT

The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

KEY POINTS

• The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.

Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study

PURPOSE

This study investigated predictors of cervical cancer (CC) recurrence from native T1 mapping, conventional imaging, and clinicopathologic metrics.

PATIENTS AND METHODS

In total, 144 patients with histopathologically confirmed CC (90 with and 54 without surgical treatment) were enrolled in this prospective study. Native T1 relaxation time, conventional imaging, and clinicopathologic characteristics were acquired. The association of quantitative and qualitative parameters with post-treatment tumor recurrence was assessed using univariate and multivariate Cox proportional hazard regression analyses. Independent risk factors were combined into a model and individual prognostic index equation for predicting recurrence risk. The receiver operating characteristic (ROC) curve determined the optimal cutoff point.

RESULTS

In total, 12 of 90 (13.3%) surgically treated patients experienced tumor recurrence. Native T1 values (X1) [hazard ratio (HR) 1.008; 95% confidence interval (CI) 1.001-1.016], maximum tumor diameter (X2) (HR 1.065; 95% CI 1.020-1.113), and parametrial invasion (X3) (HR 3.930; 95% CI 1.013-15.251) were independent tumor recurrence risk factors. The individual prognostic index (PI) of the established recurrence risk model was PI = 0.008X1 + 0.063X2 + 1.369X3. The area under the ROC curve (AUC) of the Cox regression model was 0.923. A total of 20 of 54 (37.0%) non-surgical patients experienced tumor recurrence. Native T1 values (X1) (HR 1.012; 95% CI 1.007-1.016) and lymph node metastasis (X2) (HR 4.064; 95% CI 1.378-11.990) were independent tumor recurrence risk factors. The corresponding PI was calculated as follows: PI = 0.011X1 + 1.402X2; the Cox regression model AUC was 0.921.

CONCLUSIONS

Native T1 values combined with conventional imaging and clinicopathologic variables could facilitate the pretreatment prediction of CC recurrence.

The use of quantitative T1-mapping to identify cells and collagen fibers in rectal cancer

Aim

This study aimed to explore the value of T1 mapping in assessing the grade and stage of rectal adenocarcinoma and its correlation with tumor tissue composition.

Methods

Informed consent was obtained from all rectal cancer patients after approval by the institutional review board. Twenty-four patients (14 women and 10 men; mean age, 64.46 years; range, 35 - 82 years) were enrolled in this prospective study. MRI examinations were performed using 3.0T MR scanner before surgery. HE, immunohistochemical, and masson trichrome-staining was performed on the surgically resected tumors to assess the degree of differentiation, stage, and invasion. Two radiologists independently analyzed native T1 and postcontrast T1 for each lesion, and calculated the extracellular volume (ECV) was calculated from T1 values. Intraclass correlation coefficient (ICC) and Bland-Altman plots were applied to analyze the interobserver agreement of native T1 values and postcontrast T1 values. Student's t-test and one-way analysis of variance (ANOVA) were used to test the differences between T1 mapping parameters and differentiation types, T and N stages, and venous and neural invasion. Pearson correlation coefficients were used to analyze the correlation of T1 mapping extraction parameters with caudal type homeobox 2 (CDX-2), Ki-67 index, and collagen expression.

Results

Both the native and postcontrast T1 values had an excellent interobserver agreement (ICC 0.945 and 0.942, respectively). Postcontrast T1 values indicated significant differences in venous invasion (t=2.497, p=0.021) and neural invasion (t=2.254, p=0.034). Pearson's correlation analysis showed a significant positive correlation between native T1 values and Ki-67 (r=-0.407, p=0.049). There was a significant positive correlation between ECV and collagen expression (r=0.811, p=.000) and a significant negative correlation between ECV and CDX-2 (r=-0.465, p=0.022) and Ki-67 (r=-0.549, p=0.005).

Conclusion

Postcontrast T1 value can be used to assess venous and neural invasion in rectal cancer. ECV measurements based on T1 mapping can be used to identify cells and collagen fibers in rectal cancer.