Multiparametric MRI-based radiomics signature for preoperative estimation of tumor-stroma ratio in rectal cancer

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study

BACKGROUND

Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC).

MATERIALS AND METHODS

In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy.

RESULTS

The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not.

CONCLUSION

The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma

BACKGROUND

Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult.

PURPOSE

To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery.

MATERIAL AND METHODS

A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated.

RESULTS

The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively.

CONCLUSION

The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.

Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning

OBJECTIVE

To construct and validate a multi-dimensional model based on multiple machine leaning algorithms to predict PCLM using multi-parameter magnetic resonance (MRI) sequences with clinical and imaging parameters.

METHODS

A total of 148 PDAC retrospectively examined patients were classified as metastatic or non-metastatic based on results at 3 months after surgery. The radiomics features of the primary tumor were extracted from T2WI images, followed by dimension reduction. Then, multiple machine learning methods were used to construct models. Independent predictors were also screened using multifactor logistic regression and a nomogram was constructed in combination with the radiomics model. Area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the accuracy and reliability of the nomogram.

RESULTS

The diagnostic efficacy of the radiomics model in the training and test set was 0.822 and 0.803, sensitivity was 0.742 and 0.692, and specificity was 0.792 and 0.875, respectively. The diagnostic efficacy of the nomogram in the training and test set was 0.866 and 0.832.

CONCLUSION

A radiomics nomogram based on machine learning improved the accuracy of predicting PCLM and may be useful for early preoperative diagnosis.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma

OBJECTIVE

We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC).

METHODS

A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed.

RESULTS

The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency.

CONCLUSION

DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

BACKGROUND

To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer.

METHODS

Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms.

RESULTS

Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians.

CONCLUSION

The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics

Objective

This study aimed to explore the radiomics model based on magnetic resonance imaging (MRI) T2WI and compare the value of different machine algorithms in preoperatively predicting tumor budding (TB) grading in rectal cancer.

Methods

A retrospective study was conducted on 266 patients with preoperative rectal MRI examinations, who underwent complete surgical resection and confirmed pathological diagnosis of rectal cancer. Among them, patients from Qingdao West Coast Hospital were assigned as the training group (n=172), while patients from other hospitals were assigned as the external validation group (n=94). Regions of interest (ROIs) were delineated, and image features were extracted and dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO). Eight machine algorithms were used to construct the models, and the diagnostic performance of the models was evaluated and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as clinical utility assessment using decision curve analysis (DCA).

Results

A total of 1197 features were extracted, and after feature selection and dimension reduction, 11 image features related to TB grading were obtained. Among the eight algorithm models, the support vector machine (SVM) algorithm achieved the best diagnostic performance, with accuracy, sensitivity, and specificity of 0.826, 0.949, and 0.723 in the training group, and 0.713, 0.579, and 0.804 in the validation group, respectively. DCA demonstrated the clinical utility of this radiomics model.

Conclusion

The radiomics model based on MR T2WI can provide an effective and noninvasive method for preoperative TB grading assessment in patients with rectal cancer.

Predictive value of a nomogram based on DCE-MRI and DWI quantitative parameters and serum CEA level for risk of postoperative recurrence/metastasis of rectal cancer

BACKGROUND

The prognosis of rectal cancer is poor, and early prediction of recurrence and metastasis after radical surgery is of great significance for improving its prognosis. This study integrated multiple influencing factors such as multimodal magnetic resonance imaging (MRI) parameters, tumor markers, and clinicopathological features to develop a nomogram to provide a basis for the development of clinical intervention measures for this malignancy.

AIM

To develop a nomogram based on dynamic contrast enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) quantitative parameters and serum carcinoembryonic antigen (CEA) level, and to analyze the predictive value of this model for the risk of postoperative recurrence and metastasis of rectal cancer, so as to guide the development of clinical intervention measures for this malignancy.

METHODS

A total of 120 patients who underwent laparoscopic-assisted radical resection of rectal cancer at our hospital from March 1, 2019 to February 28, 2022 were selected as research subjects. According to the presence of recurrence/metastasis within 1 year after surgery, the patients were divided into a recurrence/metastasis group (n = 29) and a no recurrence/metastasis group (n = 91). The relevant parameters [apparent diffusion coefficient (ADC), transfer rate constant (Ktrans), blood return constant (Kep), and extravascular extracellular space volume fraction (Ve)] of multimodal MRI imaging techniques were compared between the two groups to analyze their predictive value for postoperative recurrence/metastasis. Univariate analysis with Lasso model screening for predictive factors related to postoperative recurrence/metastasis was performed, and logistic regression analysis was used to analyze the influencing factors of postoperative recurrence/metastasis. A nomogram was developed based on the influencing factors identified, and the predictive value of the model for postoperative recurrence/metastasis was assessed. Calibration curve and decision curve analysis (DCA) were used to verify the calibration degree and clinical effectiveness of the model, respectively.

RESULTS

ADC in the recurrence/metastasis group was lower than that in the no recurrence/metastasis group, while Ktrans and Kep were higher than those in the no recurrence/metastasis group (P < 0.05). Obstruction, degree of differentiation, clinical stage, lymph node metastasis, postoperative CEA, ADC, Ktrans, and Kep were identified to be independent influencing factors on postoperative recurrence/metastasis (P < 0.05). The area under the curve of the nomogram was higher than that of ADC, Ktrans, and Kep combined (P < 0.05), and the nomogram had good calibration and clinical efficacy.

CONCLUSION

The nomogram developed based on DCE-MRI and DWI quantitative parameters and serum CEA level has appreciated predictive value for postoperative recurrence/metastasis of rectal cancer, and clinical intervention measures can be formulated according to these high risk factors to reduce the risk of postoperative recurrence/metastasis.

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

Ultra-high b-Value DWI in predicting progression risk of locally advanced rectal cancer: a comparative study with routine DWI

BACKGROUND

The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI.

METHODS

This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm²) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm²) were processed with mono-exponential model to generate ADC and ADCuh, respectively. The performance of the ADCuh was compared with ADC in 3-year progression free survival (PFS) assessment using time-dependent ROC and Kaplan-Meier curve. Prognosis model was constructed with ADCuh, ADC and clinicopathologic factors using multivariate COX proportional hazard regression analysis. The prognosis model was assessed with time-dependent ROC, decision curve analysis (DCA) and calibration curve.

RESULTS

A total of 112 patients with LARC (TNM-stage II-III) were evaluated. ADCuh performed better than ADC for 3-year PFS assessment (AUC = 0.754 and 0.586, respectively). Multivariate COX analysis showed that ADCuh and ADC were independent factors for 3-year PFS (P < 0.05). Prognostic model 3 (TNM-stage + extramural venous invasion (EMVI) + ADCuh) was superior than model 2 (TNM-stage + EMVI + ADC) and model 1 (TNM-stage + EMVI) for 3-year PFS prediction (AUC = 0.805, 0.719 and 0.688, respectively). DCA showed that model 3 had higher net benefit than model 2 and model 1. Calibration curve demonstrated better agreement of model 1 than model 2 and model 1.

CONCLUSIONS

ADCuh from UHBV-DWI performed better than ADC from routine DWI in predicting prognosis of LARC. The model based on combination of ADCuh, TNM-stage and EMVI could help to indicate progression risk before treatment.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.

Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study

OBJECTIVES

To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients.

METHODS

A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining. Clinical factors and laboratory data associated with MSI status were analyzed. The imaging data of 100 patients from one of the hospitals were used as the training set. The remaining 99 patients from the other two hospitals were used as the external validation set. The regions of interest (ROIs) were delineated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) sequence to extract the radiomics features. The Tree-based approach was used for feature selection. The models were constructed based on the four single sequences and a combination of the four sequences using the random forest (RF) algorithm. The external validation set was used to verify the generalization ability of each model. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each model.

RESULTS

In the four single-series models, the CE-T1WI model performed the best. The AUCs of the T1WI, T2WI, DWI, and CE-T1WI prediction models in the training set were 0.74, 0.71, 0.71, and 0.78, respectively, while in the external validation set, the corresponding AUCs were 0.67, 0.66, 0.70, and 0.77. The prediction and generalization performance of the combined model of multi-sequences was comparable to that of the CE-T1WI model and it was better than that of the remaining three single-series models, with AUC values of 0.78 and 0.78 in the training and validation sets, respectively.

CONCLUSION

The established radiomics models based on CE-T1WI or multiparametric MRI have similar predictive performance. They have the potential to predict MSI status in rectal cancer patients.

KEY POINTS

• A radiomics model for the prediction of MSI status in patients with rectal cancer was established and validated using external validation. • The models based on CE-T1WI or multiparametric MRI have better predictive performance than those based on single unenhanced sequence images. • The radiomics model has the potential to suggest MSI status in rectal cancer patients; however, it is not yet a substitute for histological confirmation.

Endorectal Ultrasound Shear-Wave Elastography of Complex Rectal Adenoma and Early Rectal Cancer

Purpose: To investigate the diagnostic performance of endorectal ultrasound (ERUS), shear-wave elastography (SWE), and magnetic resonance imaging (MRI) in patients with a complex rectal adenoma or an early rectal cancer, i.e., T1 or T2 adenocarcinoma in a clinical setting, and to evaluate the association between SWE and stromal fraction (SF) and apparent diffusion coefficient (ADC) and SF. Method: This prospective study included patients undergoing ERUS and SWE for a rectal tumor subsequently confirmed by histopathology to be an adenoma or a T1 or T2 adenocarcinoma. The accuracy of the imaging methods was assessed by comparing the T category as determined by ERUS and MRI with histopathology, which served as the gold standard. SF was assessed on surgical specimens. Results: A total of 86 patients were included. Of these, 62 patients had adenomas and 24 patients had carcinomas, of which 11 were T1 tumors and 13 were T2 tumors. ERUS and MRI yielded sensitivity, specificity, and accuracy of 0.79 and 0.73, 0.95 and 0.90, and 0.86 and 0.78, respectively, for discrimination between benign and malignant lesions. The area under the receiver operating characteristics curve for SWE was 0.88, and with a cut-off value of 40 kPa the sensitivity, specificity, and accuracy were 0.79, 0.89, and 0.86, respectively. There was a positive correlation between SF and SWE with a p-value of <0.001 and a negative correlation between SF and ADC with a p-value of 0.011. Conclusion: Both ERUS and MRI classified T categories with a high accuracy; however, ERUS classified more adenomas correctly than MRI. In this small population, SWE could differentiate an adenoma from early carcinoma. SF was correlated with both SWE and ADC, as increasing SF tended to yield higher SWE and lower ADC values.

Correlation between apparent diffusion coefficient and tumor-stroma ratio in hybrid 18F-FDG PET/MRI: preliminary results of a rectal cancer cohort study

Background

To explore possible correlations between the tumor-stroma ratio (TSR) and different imaging features of fluorine-18-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (¹⁸F-FDG PET/MRI) in untreated rectal cancer patients.

Methods

A patients with rectal cancer were included in this study. All participants were examined preoperatively with whole-body ¹⁸F-FDG PET/MRI. Two pathologists evaluated the TSR of tumors together. Apparent diffusion coefficient (ADC) values and PET-related parameters of the primary lesions were measured and compared between the stroma-high and stroma-low groups. Pearson's correlation or Spearman's rank correlation were used to evaluate the correlation between the ADC values, PET-related parameters, and pathological indices.

Results

Our results showed that in the untreated rectal cancer patients, the ADC mean values correlated with the TSR (r=0.327; P=0.007), and stroma-high (low TSR) rectal cancer corresponded to relatively lower ADC mean values (813.54±88.68 vs. 879.92±133.18; P=0.018). The ADC mean and ADC minimum (ADCmin) values were found to be negatively correlated with the pathological T stages (r=-0.384, P=0.001; r=-0.416, P=0.001, respectively) as well as the largest tumor diameters (r=-0.340, P=0.005; r=-0.314, P=0.010, respectively) of rectal cancer. In addition, the pathological T stages correlated with all PET-related metabolic parameters, including mean standard uptake value (SUV), maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) (r=0.338, P=0.006; r=0.350, P=0.004; r=0.326, P=0.007; and r=0.472, P<0.001, respectively). Our results also identified associations between the ADCmin values and SUVmean, SUVmax, and TLG (r=-0.335, P=0.006; r=-0.343, P=0.005; and r=-0.343, P=0.005, respectively). However, there were no statistical correlations between the PET/MRI parameters and the immunohistochemical (IHC) results.

Conclusions

This study indicated that the intratumoral heterogeneity measured by PET/MRI may reflect characteristics of the tumor microenvironment. Hence, PET/MRI parameters might be helpful in predicting tumor aggressiveness and prognosis.

Correlation between diffusion kurtosis and intravoxel incoherent motion derived (IVIM) parameters and tumor tissue composition in rectal cancer: a pilot study

PURPOSE

To correlate non-invasive quantitative diffusion kurtosis imaging (DKI) and intravoxel incoherent motion-derived (IVIM) parameters with rectal cancer composition assessed by the expression of caudal-type homeobox 2 (CDX-2), Vimentin (VIM), CD34 and Ki-67 on resected tissues, as well as the tumor stroma ratio (TSR) and the results of H&E and Masson staining.

MATERIALS AND METHODS

A prospective study of 26 patients with rectal cancer who underwent magnetic resonance (MR) imaging, including DKI with 4 b values and IVIM at 3.0 T prior to surgery. Primary tumor was harvested and fixed for H&E, immunohistochemistry and Masson staining. One-way ANOVA was used to test the differences. Pearson correlation coefficients and multiple linear regression analyses were applied to evaluation the correlations.

RESULTS

The apparent diffusion coefficient (ADC_DKI) and MK_DKI all exhibited significant differences in subgroups with different T stages (P < 0.05) and among high- and low- grade rectal cancer (P < 0.05). MD_DKI showed a moderate negative correlation with CDX-2 (r = - 0.42, P = 0.040) and a moderate positive correlation with CD34 (r = 0.42, P = 0.041). ADC_IVIM exhibited a moderate positive correlation with Masson staining (r = 0.426, P = 0.048) D_IVIM showed a moderate negative correlation with CDX-2 (r = - 0.58, P = 0.005). [Formula: see text] showed a moderate positive correlation with VIM (r = 0.445, P = 0.033).

CONCLUSION

ADC_DKI and MK_DKI demonstrated a higher correlation with T stages and histologic grades. MD_DKI showed significant correlations with CDX-2 and CD34. ADC_IVIM showed significant correlation with Masson. D_IVIM showed significant correlations with CDX-2 and [Formula: see text] showed significant correlation with VIM. These findings should be validated in a larger study.

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.

CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

Purpose

To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).

Materials and Methods

In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.

Results

We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.

Conclusions

The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods

The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of patients with rectal cancer (RC). A total of 454 consecutive patients with pathologically confirmed RC who underwent preoperative MRI between January 2018 and December 2019 were retrospectively analyzed. RC patients were grouped according to whether the muscularis propria was penetrated. Two radiologists segmented lesions, respectively, by minimum delineation (Method 1) and maximum delineation (Method 2), after which radiomics features were extracted. Inter- and intraclass correlation coefficient (ICC) of all features was evaluated. After feature reduction, the support vector machine (SVM) was trained to build a prediction model. The diagnostic performances of models were determined by receiver operating characteristic (ROC) curves. Then, the areas under the curve (AUCs) were compared by the DeLong test. Decision curve analysis (DCA) was performed to evaluate clinical benefit. Finally, 317 patients were assessed, including 152 cases in the training set and 165 cases in the validation set. Moreover, 1288/1409 (91.4%) features of Method 1 and 1273/1409 (90.3%) features of Method 2 had good robustness (P < 0.05). The AUCs of Model 1 and Model 2 were 0.808 and 0.903 in the validation set, respectively (P = 0.035). DCA showed that the maximum delineation yielded more net benefit. MRI-based radiomics models derived from two segmentation methods demonstrated good performance in the preoperative T staging of RC. The minimum delineation had better stability in feature selection, while the maximum delineation method was more clinically beneficial.

Clinical significance of tumor-stroma ratio in head and neck cancer: a systematic review and meta-analysis

BACKGROUND

The clinical significance of tumor-stroma ratio (TSR) has been examined in many tumors. Here we systematically reviewed all studies that evaluated TSR in head and neck cancer.

METHODS

Four databases (Scopus, Medline, PubMed and Web of Science) were searched using the term tumo(u)r-stroma ratio. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) were followed.

RESULTS

TSR was studied in nine studies of different subsites (including cohorts of nasopharyngeal, oral, laryngeal and pharyngeal carcinomas). In all studies, TSR was evaluated using hematoxylin and eosin staining. Classifying tumors based on TSR seems to allow for identification of high-risk cases. In oral cancer, specifically, our meta-analysis showed that TSR is significantly associated with both cancer-related mortality (HR 2.10, 95%CI 1.56-2.84) and disease-free survival (HR 1.84, 95%CI 1.38-2.46).

CONCLUSIONS

The assessment of TSR has a promising prognostic value and can be implemented with minimum efforts in routine head and neck pathology.