Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps

Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics

BACKGROUND

Lymphovascular invasion (LVI) status plays an important role in treatment decision-making in rectal cancer (RC). Intravoxel incoherent motion (IVIM) imaging has been shown to detect LVI; however, making better use of IVIM data remains an important issue that needs to be discussed.

PURPOSE

We proposed to explore the best way to use IVIM quantitative parameters and images to construct radiomics models for the noninvasive detection of LVI in RC.

METHODS

A total of 83 patients (LVI negative (LVI-): LVI positive (LVI+) = 51:32) with postoperative pathology-confirmed LVI status in RC were divided into a training group (n = 58) and a validation group (n = 25). Images were acquired from a 3.0 Tesla machine, including oblique axial T2 weighted imaging (T2WI) and IVIM with 11 b values. The ADC, D, D* and f values were measured on IVIM maps. The ROIs of tumors were delineated on T2WI, DWI, ADCmap , and Dmap images, and three mapping methods were used: ROIs_mapping from DWI, ROIs_mapping from ADCmap , and ROIs_mapping from Dmap . Three-dimensional radiomics features were extracted from the delineated ROIs. Multivariate logistic regression was used for radiomics feature selection. Radiomics models based on different mapping methods were developed. Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were used to evaluate the performance of the models.

RESULTS

Model B, which was constructed with radiomics features from ADCmap , Dmap and fmap by "ROIs_mapping from DWI" and T2WI (AUC 0.894), performed better than other models based on single sequence (AUC 0.600-0.806) and even better than Model A, which was based on "ROIs_mapping from ADC" and T2WI (AUC 0.838). Furthermore, an integrated model was constructed with Model B and the IVIM parameter (f value) with an AUC of 0.920 (95% CI: 0.820-1.000), which was higher than that of Model B, in the validation group.

CONCLUSIONS

The integrated model incorporating the radiomics features and IVIM parameters accurately detected LVI of RC. The "ROIs_mapping from DWI" method provided the best results.

Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer

OBJECTIVES

To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer.

METHODS

This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T₂-weighted images, diffusion-weighted images and enhanced T₁-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance.

RESULTS

The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice.

CONCLUSION

A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery

Simple Summary

The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery.

Abstract

We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.

The Preoperative Diagnostic Performance of Multi-Parametric Quantitative Assessment in Rectal Carcinoma: A Preliminary Study Using Synthetic Magnetic Resonance Imaging

Objective

Synthetic MRI (SyMRI) can reconstruct different contrast-weighted images(T1, T2, PD) and has shorter scan time, easier post-processing and better reproducibility. Some studies have shown splendid correlation with conventional mapping techniques and no degradation in the quality of syMRI images compared with conventional MRI. It is crucial to select an individualized treatment plan based on the preoperative images of rectal carcinoma (RC). We tried to explore the feasibility of syMRI on T, N stage and extramural vascular invasion (EMVI) of rectal cancer.

Materials and Methods

A total of 100 patients (37 females and 63 males) diagnosed with rectal carcinoma were enrolled. All the patients underwent preoperative pelvic MR examinations including conventional MR sequence and synthetic MRI. Two radiologists evaluated the MRI findings of each rectal carcinoma and EMVI score in consensus. The values for T1, T2 relaxation times and PD value were measured in tumor(ROI-1) and pararectal fat space(ROI-2) and analyzed independently. A receiver operating characteristic (ROC) analysis was performed. Correlations between the T1, T2 and PD values and EMVI score were also evaluated.

Results

Compared with the normal rectal wall, the values of T1 and T2 relaxation times of the tumor were significantly higher (P <0.001). There was no statistically significant difference in the PD value (P >0.05). As for ROI, the ROI of pararectal fat space(ROI-2) had better significance than rectal cancer lesion (ROI-1). T2 value of ROI-1 and T1 value of ROI-2 were higher in the pEMVI positive group than in the negative group (P=0.002 and 0.001) and T1 value of ROI-2 had better performance with an AUC of 0.787, (95% CI:0.693- 0.882). T1 value, T2 value and PD value from ROI-2 were effective for both T and N stage of rectal cancer. High-grade pathological stage had showed higher T1 value (P_{T stage}=0.013,P_{N stage}=0.035), lower T2 value (P_{T stage}=0.025,P_{N stage}=0.034) and lower PD value (P_{T stage}=0.017). We also enrolled the characteristics with P < 0.05 in the combined model which had better diagnostic efficacy. A significant positive correlation was found between the T1 value of pararectal fat space(ROI-2) and EMVI score (r value = 0.519, P<0.001). The T2 value(r=0.213,P=0.049) and PD value(r=0.354,P=0.001) from ROI-1 was correlated with EMVI score. Correlation analysis did not show any significant associations between T2 value of tumor, T2, PD values of pararectal fat space and EMVI scores.

Conclusion

Synthetic MRI can provide multi-parameter quantitative image maps with a easier measurement and slightly shorter acquisition time compared with conventional MRI. The measurement of multi-parametric quantitative values contributes to diagnosing the tumor and evaluating T stage, N stage and EMVI. It has the potential to be used as a preoperative diagnostic and grading technique in rectal carcinoma.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Simple Summary

Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.

Abstract

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

The added value of full and reduced field-of-view apparent diffusion coefficient maps for the evaluation of extramural venous invasion in rectal cancer

OBJECTIVE

To investigate the added value of the quantitative analysis of full and reduced field-of-view apparent diffusion coefficient (fADC and rADC) maps for evaluating extramural venous invasion (EMVI) in rectal cancer.

MATERIALS AND METHODS

A total of 94 rectal cancer patients who underwent direct surgical resection were enrolled in this prospective study. The EMVI status of each patient was evaluated on T2-weighted imaging. The mean values of fADC and rADC within the whole tumor were obtained, and histogram parameters were also extracted. Multivariate binary logistic regression analysis was used to analyze independent predictors of EMVI and construct combined models. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic performance.

RESULTS

The energy, skewness, total energy, and kurtosis of fADC map, and the energy and total energy of rADC map were significantly different between the EMVI-positive and EMVI-negative groups (all P < 0.05). Multivariate logistic regression analysis revealed that kurtosis of fADC and circumferential percentage of tumor were independent predictors of EMVI (odds ratio 1.684 and 2.647, P = 0.020 and 0.009). These two parameters combined with subjective evaluation demonstrated the superior diagnostic performance with the area under the ROC curve, sensitivity, specificity, and accuracy of 0.841 (95% CI 0.752-0.909), 0.739, 0.803, and 0.809, respectively.

CONCLUSION

Whole-tumor histogram analysis of ADC map could potentially provide additional information to improve the diagnostic efficiency for assessing EMVI in rectal cancer, which may be beneficial for treatment decision-making.

Preliminary Study on Predicting Pathological Staging and Immunohistochemical Markers of Rectal Cancer Based on ADC Histogram Analysis

OBJECTIVE

To analyze the correlation between histogram parameters of ADC and pathological staging of rectal cancer and CD31, CD2-40, S-100, and to explore its predictive value.

MATERIALS AND METHODS

MRI findings of 60 patients with surgically and pathologically proved rectal cancer were analyzed retrospectively. Patients were divided into pT1-2, pT3-4, pN0, pN1-2, G1-2 and G3 groups according to TNM staging of UICC tumors (2019) and WHO classification of digestive system tumors (2019). Cases were divided into CD31 (+) and CD31 (-), CD2-40 (+) and CD4-20 (-), S-100 (+) and S-100 (-) groups according to the expression of immunohistochemical markers. The ROI was delineated layer by layer on the ADC images by Firevoxel software, and the histogram parameters were extracted. The histogram parameters (ADC mean, ADC minimum, ADC maximum, ADC mode, ADC quartile), skewness, kurtosis and entropy were compared between each group. The bivariate logistic regression model was used to predict the tumor staging and immunohistochemical results.

RESULTS

1. ADC10th, ADC mean and Entropy were higher than pT3-4, ADC mean was higher than pT1-2, Entropy was lower than pT1-2, ADC10th, ADC25th, ADC50th, ADC mean and ADC mode were lower than pT3-40 (-) in CD2-40 (+) group, and the difference was statistically significant (p < 0.05); 2. The lower area of the curve (AUC) of rectal cancer pT, pN and CD2-40 (+) is 0.952 (0.892-1.000), 0.882 (0.791-0.972), 0.913 (0.840-0.985); 3. In the logistic regression model, higher ADC, Ropy and higher pN stages are independent predictors of tumor pT stages (OR = 1.156, 1.144,111.528); p = 0.045, 0.048, 0.002); higher Ropy and lower pT stages are independent predictors of tumor pN stages in the model (OR = 73.939, 0.024; p = 0.019, 0.001); higher ADC and lower differentiation are independent predictors of tumor CD2-40 stages in the model (ADC = 1.17, 0.048, 0.011); and higher Ropy and lower pT stages are independent predictors of tumor CD2-40 stages in the model (ADC = 1.096, 0.094, 0.044).

CONCLUSION

Histogram analysis based on ADC images has potential value in predicting the pathologic stage and immunohistochemical markers of rectal cancer, and logistic regression model has better diagnostic efficacy than single parameter.

Texture Analysis in the Assessment of Rectal Cancer: Comparison of T2WI and Diffusion-Weighted Imaging

Texture analysis (TA) techniques derived from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps of rectal cancer can both achieve good diagnosis performance. This study was to compare TA from T2WI and ADC maps between different pathological T and N stages to confirm which TA analysis is better in diagnosis performance. 146 patients were enrolled in this study. Tumor TA was performed on every patient's T2WI and ADC maps, respectively; then, skewness, kurtosis, uniformity, entropy, energy, inertia, and correlation were calculated. Our results demonstrated that those significant different parameters derived from T2WI had better diagnostic performance than those from ADC maps in differentiating pT3b-4 and pN1-2 stage tumors. In particular, the energy derived from T2WI was an optimal parameter for diagnostic efficiency. High-resolution T2WI plays a key point in the local stage of rectal cancer; thus, TA derived from T2WI may be a more useful tool to aid radiologists and surgeons in selecting treatment.

Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer

Background

An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE).

Methods

A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status.

Results

Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485).

Conclusions

The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10⁻⁵; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Prediction of pathological prognostic factors of rectal cancer by relaxation maps from synthetic magnetic resonance imaging

PURPOSE

To explore the feasibility of relaxation maps from synthetic MRI for predicting pathological prognostic factors of rectal cancer (RC) and to compare the predictive performance of quantitative values and conventional subjective evaluation.

MATERIAL AND METHODS

A total of 94 patients with pathologically proven RC who underwent direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI including synthetic MRI was performed. The mean T₁, T₂, and PD value of the whole tumor was obtained to preoperatively assess the pathological T stage, N stage, extramural venous invasion (EMVI), differentiation, and perineural invasion. Receiver operating characteristic curves were used to explore the predictive performance for assessing the prognostic factors. The T stage, N stage and EMVI status on conventional T2WI were evaluated and compared with the quantitative values.

RESULTS

The T₂ value decreased significantly in patients with positive perineural invasion, lymph node metastasis (LNM), EMVI, and higher T stage RC (p =  0.007 and < 0.001). The T₁ value of LNM and EMVI positive groups was significantly lower than those of the negative groups (p =  0.034 and 0.011). For predicting N stage and EMVI, the T₂ value demonstrated good performance with an AUC of 0.883 (95 % confidence interval, CI, 0.801-0.940) and 0.821 (95 % CI, 0.729-0.893); the T₂ value was superior to the T₁ value and subjective evaluation of radiologists (all p < 0.05).

CONCLUSION

Synthetic MRI is a promising tool for noninvasive evaluation of prognostic factors of RC by generating relaxation maps.

Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer

Background: Accurate evaluation of local invasion (T-stage) of rectal cancer is essential for treatment planning. A search of PubMed database indicated that the correlation between texture features from T2-weighted magnetic resonance imaging (T2WI) (MRI) and T-stage has not been explored extensively.

Purpose: To evaluate the performance of texture analysis using sagittal fat-suppression combined with transverse T2WI for determining T-stage of rectal cancer.

Methods: One hundred and seventy-four rectal cancer cases who underwent preoperative MRI were retrospectively selected and divided into high (T3/4) and low (T1/2) T-stage groups. Texture features were, respectively, extracted from sagittal fat-suppression and transverse T2WI images. Univariate and multivariate analyses were conducted to determine T-stage. Discrimination performance was assessed by receiver operating characteristic (ROC) analysis.

Results: For univariate analysis, the best performance in differentiating T1/2 from T3/4 tumors was achieved from transverse T2WI, and the area under the ROC curve (AUC) was 0.740. For multivariate analysis, the logical regression model incorporating the independent predictors achieved an AUC of 0.789.

Conclusions: Texture features from sagittal fat-suppression combined with transverse T2WI presented moderate association with T-stage of rectal cancer. These findings may be valuable in selecting optimum treatment strategy.

MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry

AIM

To investigate the role of diffusion-weighted imaging (DWI), T2-weighted (W) imaging, and apparent diffusion coefficient (ADC) histogram analysis before, during, and after neoadjuvant chemoradiotherapy (CRT) in the prediction of pathological response in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) at 1.5 T was performed in 43 patients with LARC before, during, and after CRT. Tumour volume was measured on both T2-weighted (V_T2W) and on DWI at b=1,000 images (V_b,1,000) at each time point, hence the tumour volume reduction rate (ΔV_T2W and ΔV_b,1,000) was calculated. Whole-lesion (three-dimensional [3D]) first-order texture analysis of the ADC map was performed. Imaging parameters were compared to the pathological tumour regression grade (TRG). The diagnostic performance of each parameter in the identification of complete responders (CR; TRG4), partial responders (PR; TRG3) and non-responders (NR; TRG0-2) was evaluated by multinomial regression analysis and receiver operating characteristics curves.

RESULTS

After surgery, 11 patients were CR, 22 PR, and 10 NR. Before CRT, predictions of CR resulted in an ADC value of the 75th percentile and median, with good accuracy (74% and 86%, respectively) and sensitivity (73% and 82%, respectively). During CRT, the best predictor of CR was ΔV_T2W (-58.3%) with good accuracy (81%) and excellent sensitivity (91%). After CRT, the best predictors of CR were ΔV_T2W (-82.8%) and ΔV_{b, 1,000} (-86.8%), with 84% accuracy in both cases and 82% and 91% sensitivity, respectively.

CONCLUSIONS

The median ADC value at pre-treatment MRI and ΔV_T2W (from pre-to-during CRT MRI) may have a role in early and accurate prediction of response to treatment. Both ΔV_T2W and ΔV_b,1,000 (from pre-to-post CRT) can help in the identification of CR after CRT.