Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study.

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS: This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS: The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS: Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer.

PURPOSE: To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. METHODS: A total of 178 patients (mean age: 59.35, range 20-85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients' pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23-74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan-Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). RESULTS: Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077-2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281-4.396). CONCLUSIONS: We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.

Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis.

Background: Radiomics has recently received considerable research attention for providing potential prognostic biomarkers for locally advanced rectal cancer (LARC). We aimed to comprehensively evaluate the methodological quality and prognostic prediction value of radiomic studies for predicting survival outcomes in patients with LARC. Methods: The Cochrane, Embase, Medline, and Web of Science databases were searched. The radiomics quality score (RQS), Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist, the Image Biomarkers Standardization Initiative (IBSI) guideline, and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of the selected studies. A further meta-analysis of hazard ratio (HR) regarding disease-free survival (DFS) and overall survival (OS) was performed. Results: Among the 358 studies reported, 15 studies were selected for our review. The mean RQS score was 7.73±4.61 (21.5% of the ideal score of 36). The overall TRIPOD adherence rate was 64.4% (251/390). Most of the included studies (60%) were assessed as having a high risk of bias (ROB) overall. The pooled estimates of the HRs were 3.14 [95% confidence interval (CI): 2.12-4.64, P<0.01] for DFS and 3.36 (95% CI: 1.74-6.49, P<0.01) for OS. Conclusions: Radiomics has potential to noninvasively predict outcome in patients with LARC. However, the overall methodological quality of radiomics studies was low, and the adherence to the TRIPOD statement was moderate. Future radiomics research should put a greater focus on enhancing the methodological quality and considering the influence of higher-order features on reproducibility in radiomics.

Concurrent Dual-Contrast Enhancement Using Fe3O4 Nanoparticles to Achieve a CEST Signal Controllability.

Traditional T2 magnetic resonance imaging (MRI) contrast agents have defects inherent to negative contrast agents, while chemical exchange saturation transfer (CEST) contrast agents can quantify substances at trace concentrations. After reaching a certain concentration, iron-based contrast agents can "shut down" CEST signals. The application range of T2 contrast agents can be widened through a combination of CEST and T2 contrast agents, which has promising application prospects. The purpose of this study is to develop a T2 MRI negative contrast agent with a controllable size and to explore the feasibility of dual contrast enhancement by combining T2 with CEST contrast agents. The study was carried out in vitro with HCT-116 human colon cancer cells. A GE SIGNA Pioneer 3.0 T medical MRI scanner was used to acquire CEST images with different saturation radio-frequency powers (1.25/2.5/3.75/5 µT) by 2D spin echo-echo planar imaging (SE-EPI). Magnetic resonance image compilation (MAGiC) was acquired by a multidynamic multiecho 2D fast spin-echo sequence. The feasibility of this dual-contrast enhancement method was assessed by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, ζ potential analysis, inductively coupled plasma, X-ray photoelectron spectroscopy, X-ray powder diffraction, vibrating-sample magnetometry, MRI, and a Cell Counting Kit-8 assay. The association between the transverse relaxation rate r2 and the pH of the iron-based contrast agents was analyzed by linear fitting, and the linear relationship between the CEST effect in different B1 fields and pH was analyzed by the ratio method. Fe3O4 nanoparticles (NPs) with a mean particle size of 82.6 ± 22.4 nm were prepared by a classical process, and their surface was successfully modified with -OH active functional groups. They exhibited self-aggregation in an acidic environment. The CEST effect was enhanced as the B1 field increased, and an in vitro pH map was successfully plotted using the ratio method. Fe3O4 NPs could stably serve as reference agents at different pH values. At a concentration of 30 µg/mL, Fe3O4 NPs "shut down" the CEST signals, but when the concentration of Fe3O4 NPs was less than 10 µg/mL, the two contrast agents coexisted. The prepared Fe3O4 NPs had almost no toxicity, and when their concentration rose to 200 µg/mL at pH 6.5 or 7.4, they did not reach the half-maximum inhibitory concentration (IC50). Fe3O4 magnetic NPs with a controllable size and no toxicity were successfully synthesized. By combining Fe3O4 NPs with a CEST contrast agent, the two contrast agents could be imaged simultaneously; at higher concentrations, the iron-based contrast agent "shut down" the CEST signal. An in vitro pH map was successfully plotted by the ratio method. CEST signal inhibition can be used to realize the pH mapping of solid tumors and the identification of tumor active components, thus providing a new imaging method for tumor efficacy evaluation.

Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.

Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immunoscore and clinical factors to predict the postoperative outcome of CRC patients with lung metastasis. In this study, a total of 103 CRC patients having metastases limited to lung and undergoing radical lung resection were identified. Patch-level convolutional neural network training in weakly supervised manner was used to perform whole slides histopathological images survival analysis. Synthetic minority oversampling technique and support vector machine classifier were used to identify radiomics features and build predictive signature. The Immunoscore for each patient was calculated from the density of CD3+ and CD8+ cells at the invasive margin and the center of metastatic tumor which were assessed on consecutive sections of automated digital pathology. Finally, pathomics and radiomics signatures were successfully developed to predict the overall survival (OS) and disease free survival (DFS) of patients. The predicted pathomics and radiomics scores are negatively correlated with Immunoscore and they are three independent prognostic factors for OS and DFS prediction. The combined nomogram showed outstanding performance in predicting OS (AUC = 0.860) and DFS (AUC = 0.875). The calibration curve and decision curve analysis demonstrated the considerable clinical usefulness of the combined nomogram. Taken together, the developed nomogram model consisting of machine learning-pathomics signature, radiomics signature, Immunoscore and clinical features could be reliable in predicting postoperative OS and DFS of colorectal lung metastasis patients.

Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.

This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens. A two-stage deep neural network was developed based on the 3D residual network and atrous convolution module to diagnose benign and malignant GGNs (Task1) and classify between invasive adenocarcinoma (IA) and non-IA for these malignant GGNs (Task2). A multi-reader multi-case observer study with six board-certified radiologists' (average experience 11 years, range 2-28 years) participation was conducted to evaluate the model capability. DNN yielded area under the receiver operating characteristic curve (AUC) values of 0.76 ± 0.03 (95% confidence interval (CI): (0.69, 0.82)) and 0.96 ± 0.02 (95% CI: (0.92, 0.98)) for Task1 and Task2, which were equivalent to or higher than radiologists in the senior group with average AUC values of 0.76 and 0.95, respectively (p > 0.05). With the CT image slice thickness increasing from 1.15 mm ± 0.36 to 1.73 mm ± 0.64, DNN performance decreased 0.08 and 0.22 for the two tasks. The results demonstrated (1) a positive trend between the diagnostic performance and radiologist's experience, (2) the DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution decreased model performance in predicting the risks of GGNs. Once tested prospectively in clinical practice, the DNN could have the potential to assist doctors in precision diagnosis and treatment of early lung adenocarcinoma.

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

OBJECTIVE: To determine whether a radiomics signature (rad-score) outperforms ADC in TSR estimation by developing a radiomics biomarker for preoperative TSR diagnosis in rectal cancer. METHODS: This study included 149 patients (119 and 30 in the training and validation cohorts, respectively). All patients underwent T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. A rad-score was generated using the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate logistic regression. Meanwhile, the mean ADCs were calculated from ADC maps. For both the mean ADC and rad-score, binary logistic regression and Spearman correlation coefficients were used to determine associations with the TSR, and the area under the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance. The reliability of the rad-score was quantified by comparing the imaging-estimated TSR with the actual TSR of each patient. RESULTS: Both the mean ADC and rad-score were positively correlated with the TSR in the training cohort (mean ADC: p < 0.001, r = 0.566; rad-score: p < 0.001, r = 0.559) and validation cohort (mean ADC: p < 0.001, r = 0.671; rad-score: p = 0.002, r = 0.536). The rad-score, with AUCs of 0.917 (95% CI 0.869-0.965) and 0.787 (95% CI 0.602-0.972) in the training and validation cohorts, respectively, outperformed the mean ADC (training cohort: AUC = 0.776, 95% CI 0.693-0.859; validation cohort: AUC = 0.764, 95% CI 0.592-0.936) in TSR estimation. CONCLUSION: The ADC possesses potential diagnostic value for TSR estimation in rectal cancer, and the rad-score shows increased diagnostic value over the ADC and may be a promising supplemental tool for patient stratification and informing decision-making. KEY POINTS: • Tumor-stroma ratio has been verified as an independent prognostic factor for various solid tumors including rectal cancer. • The ADC and multiparametric MRI-based radiomics features were significantly and positively correlated with the tumor-stroma ratio in rectal cancer. • The radiomics signature outperformed the ADC in discriminating TSR in rectal cancer.

CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules.

Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis. Methods: Our study enrolled 196 CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction. Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC = 0.919) and validation dataset (AUC = 0.895), and good calibration. Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.

MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases.

PURPOSE: The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy. METHODS: The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy1) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy2, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis. RESULTS: A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy1, contrast, entropy2 and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602-0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814. CONCLUSION: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

Three-Dimensional CT Texture Analysis to Differentiate Colorectal Signet-Ring Cell Carcinoma and Adenocarcinoma.

PURPOSE: The objective of this research was to validate the diagnostic value of three-dimensional texture parameters and clinical characteristics in the differentiation of colorectal signet-ring cell carcinoma (SRCC) and adenocarcinoma (AC). METHODS: We retrospectively analyzed data from 102 patients with SRCC or AC confirmed by pathology, including 51 SRCC (from January 2015 to July 2019) and 51 AC patients (from January 2019 to July 2019). CT findings and clinical data, including age, gender, clinical symptoms, serological biomarkers, tumor size, and tumor location, were compared between SRCC and AC. CT texture features were quantified on portal phase images using three-dimensional analysis. A list of texture parameters was generated with MaZda software for the classification of tumors. The texture features, clinical data and CT findings were statistically analyzed for the discrimination ability of SRCC and AC, and the potential predictive parameters that may be used to differentiate the two groups were subsequently tested using the least absolute shrinkage and selection operator (LASSO) and logistic regression analyses. The receiver operating characteristic curve (ROC) provided a range of values for establishing the cutoff value, as well as the sensitivity and specificity of prediction for each significant variable. RESULTS: SRCC occurred more often in men than AC did (80.39% vs 49.02%, P < 0.01). The patients were younger in the SRCC group than in the AC group, without a statistically significant difference (55.84 vs 59.20 years, P = 0.216). There were no significant differences in the clinical symptoms, tumor size, or tumor location between the two groups (P=0.505, P=0.19, P=0.843, respectively). The elevation of serological biomarker CA724 was more common in SRCC than in AC (P< 0.001). Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg were lower in the SRCC group than in the AC group during the portal phase, with the areas under curve (AUCs) of 0.892-0.929, sensitivity of 76.5-84.3% and specificity of 88.2-96.1%. In the differentiation between SRCC and AC, the 1-NN minimal classification error (MCR) was 29.41%. CONCLUSION: Three-dimensional texture parameters, including Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg, exhibited a favorable discriminatory ability to distinguish SRCC from AC.

A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules.

OBJECTIVES: To develop and validate a clinical-radiomics nomogram for preoperative prediction of lung metastasis for colorectal cancer (CRC) patients with indeterminate pulmonary nodules (IPN). METHODS: 194 CRC patients with lung nodules were enrolled in this study (136 in the training cohort and 58 in the validation cohort). To evaluate the probability of lung metastasis, we developed three models, the clinical model with significant clinical risk factors, the radiomics model with radiomics features constructed by the least absolute shrinkage and selection operator algorithm, and the clinical-radiomics model with significant variables selected by the stepwise logistic regression. The Akaike information criterion (AIC) was used to compare the relative strength of different models, and the area under the curve (AUC) was used to quantify the predictive accuracy. The nomogram was developed based on the most appropriate model. Decision-curve analysis was applied to assess the clinical usefulness. RESULTS: The clinical-radiomics model (AIC = 98.893) with the lowest AIC value compared with that of the clinical-only model (AIC = 138.502) or the radiomics-only model (AIC = 116.146) was identified as the best model. The clinical-radiomics nomogram was also successfully developed with favourable discrimination in both training cohort (AUC = 0.929, 95% CI: 0.885-0.974) and validation cohort (AUC = 0.922, 95% CI: 0.857-0.986), and good calibration. Decision-curve analysis confirmed the clinical utility of the clinical-radiomics nomogram. CONCLUSIONS: In CRC patients with IPNs, the clinical-radiomics nomogram created by the radiomics signature and clinical risk factors exhibited favourable discriminatory ability and accuracy for a metastasis prediction. KEY POINTS: • Clinical features can predict lung metastasis of colorectal cancer patients. • Radiomics analysis outperformed clinical features in assessing the risk of pulmonary metastasis. • A clinical-radiomics nomogram can help clinicians predict lung metastasis in colorectal cancer patients.