Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm-enhanced artificial neural network-based CT radiomics signature.

OBJECTIVE: The stratification of microsatellite instability (MSI) status assists clinicians in making treatment decisions for colorectal cancer (CRC) patients. This study aimed to establish a CT-based radiomics signature to predict MSI status in patients with CRC. METHODS: A total of 837 CRC patients who underwent preoperative enhanced CT and had available MSI status data were recruited from two hospitals. Radiomics features were extracted from segmented tumours, and a series of data balancing and feature selection strategies were used to select MSI-related features. Finally, an MSI-related radiomics signature was constructed using a genetic algorithm-enhanced artificial neural network model. Combined and clinical models were constructed using multivariate logistic regression analyses by integrating the clinical factors with or without the signature. A Kaplan-Meier survival analysis was conducted to explore the prognostic information of the signature in patients with CRC. RESULTS: Ten features were selected to construct a signature which showed robust performance in both the internal and external validation cohorts, with areas under the curves (AUC) of 0.788 and 0.775, respectively. The performance of the signature was comparable to that of the combined model (AUCs of 0.777 and 0.767, respectively) and it outperformed the clinical model constituting age and tumour location (AUCs of 0.768 and 0.623, respectively). Survival analysis demonstrated that the signature could stratify patients with stage II CRC according to prognosis (HR: 0.402, p = 0.029). CONCLUSIONS: This study built a robust radiomics signature for identifying the MSI status of CRC patients, which may assist individualised treatment decisions. KEY POINTS: • Our well-designed modelling strategies helped overcome the problem of data imbalance caused by the low incidence of MSI. • Genetic algorithm-enhanced artificial neural network-based CT radiomics signature can effectively distinguish the MSI status of CRC patients. • Kaplan-Meier survival analysis demonstrated that our signature could significantly stratify stage II CRC patients into high- and low-risk groups.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer.

OBJECTIVES: To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction. METHODS: In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55). RESULTS: iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001). CONCLUSIONS: iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection. KEY POINTS: • Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram.

Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Densenet to detect ECG characteristic points, including the onset, peak and termination points of P-wave, QRS complex wave and T-wave. We extracted high-level features of ECG heartbeats from the QT Database (QTDB) and two other larger datasets, MIT-BIH Arrhythmia Database (MITDB) and MIT-BIH Normal Sinus Rhythm Database (NSRDB) with no human-annotated labels as pre-training. By applying different transformations to ECG signals, the task of discriminating signals before and after transformation was defined as the pretext task. Subsequently, the convolutional layer was frozen and the weights of the self-supervised network were transferred to the downstream task of characteristic point localizations on heart beats in the QT dataset. Finally, the mean ± standard deviation of the detection errors of our proposed self-supervised learning method in QTDB for detecting the onset, peak, and termination points of P-waves, the onset and termination points of QRS waves, and the peak and termination points of T-waves were -0.24 ± 10.04, -0.48 ± 11.69, -0.28 ± 10.19, -3.72 ± 8.18, -4.12 ± 13.54, -0.68 ± 20.42, and 1.34 ± 21.04. The results show that the deep learning network based on the self-supervised framework constructed in this manuscript can accurately detect the feature points of a heartbeat, laying the foundation for automatic extraction of key information related to ECG-based diagnosis.

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.

Objective: This study aimed to establish a method to predict the overall survival (OS) of patients with stage I-III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification. Methods: We retrospectively identified 161 consecutive patients with stage I-III CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction. Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio (HR)=6.670; 95% confidence interval (95% CI): 3.433-12.956; P<0.001), external validation cohort 1 (HR=2.866; 95% CI: 1.646-4.990; P<0.001) and external validation cohort 2 (HR=3.342; 95% CI: 1.289-8.663; P=0.002). Incorporating the EcoRad signature into the prediction model presented a higher prediction ability (P<0.001) with respect to the C-index (0.813, 95% CI: 0.804-0.822 in the training cohort; 0.758, 95% CI: 0.751-0.765 in the external validation cohort 1; and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis (TNM) system, as well as a better calibration, improved reclassification and superior clinical usefulness. Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage I-III CRC patients.

A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer.

BACKGROUND: Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II-III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC. METHODS: Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scoring system, named stroma-immune score, was established by quantifying the density of CD3+ and CD8+ T-cells infiltration in the stroma region. RESULTS: Patients with higher stroma-immune scores had much longer survival. In the development group, 5-year survival rates of the low and high scores were 55.7% and 80.8% (hazard ratio [HR] for high vs. low 0.39, 95% confidence interval [CI] 0.24-0.63, P < 0.001). These results were confirmed in the internal and external validation groups with 5-year survival rates of low and high scores were 57.1% and 78.8%, 63.9% and 88.9%, respectively (internal: HR for high vs. low 0.49, 95% CI 0.28-0.88, P = 0.017; external: HR for high vs. low 0.35, 95% CI 0.15-0.83, P = 0.018). The combination of stroma-immune score and tumor-node-metastasis (TNM) stage showed better discrimination ability for survival prediction than using the TNM stage alone. CONCLUSIONS: We proposed a stroma-immune score via a deep learning-based pipeline to quantify CD3+ and CD8+ T-cells densities within the stroma region on WSIs of CRC and further predict survival.

The value of the tumour-stroma ratio for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer: a case control study.

BACKGROUND: The tumour-stroma ratio (TSR) is recognized as a practical prognostic factor in colorectal cancer. However, TSR assessment generally utilizes surgical specimens. This study aims to investigate whether the TSR evaluated from preoperative biopsy specimens by a semi-automatic quantification method can predict the response after neoadjuvant chemoradiotherapy (nCRT) of patients with locally advanced rectal cancer (LARC). METHODS: A total of 248 consecutive patients diagnosed with LARC and treated with nCRT followed by resection were included. Haematoxylin and eosin (HE)-stained sections of biopsy specimens were collected, and the TSR was evaluated by a semi-automatic quantification method and was divided into three categories, using the cut-offs determined in the whole cohort to balance the proportion of patients in each category. The response to nCRT was evaluated on the primary tumour resection specimen by an expert pathologist using the four-tier tumour regression grade (TRG) system. RESULTS: The TSR can discriminate patients that are major-responders (TRG 0-1) from patients that are non-responders (TRG 2-3). Patients were divided into stroma-low (33.5%), stroma-intermediate (33.9%), and stroma-high (32.7%) groups using 56.3 and 72.8% as the cutoffs. In the stroma-low group, 58 (69.9%) patients were major-responders, and only 39 (48.1%) patients were considered major-responders in the stroma-high group (P = 0.018). Multivariate analysis showed that the TSR was the only pre-treatment predictor of response to nCRT (adjusted odds ratio 0.40, 95% confidence interval 0.21-0.76, P = 0.002). CONCLUSION: An elevated TSR in preoperative biopsy specimens is an independent predictor of nCRT response in LARC. This semi-automatic quantified TSR could be easily translated into routine pathologic assessment due to its reproducibility and reliability.

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set.

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

A radiomics prognostic scoring system for predicting progression-free survival in patients with stage IV non-small cell lung cancer treated with platinum-based chemotherapy.

OBJECTIVE: To develop and validate a radiomics prognostic scoring system (RPSS) for prediction of progression-free survival (PFS) in patients with stage IV non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy. METHODS: In this retrospective study, four independent cohorts of stage IV NSCLC patients treated with platinum-based chemotherapy were included for model construction and validation (Discovery: n=159; Internal validation: n=156; External validation: n=81, Mutation validation: n=64). First, a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography (CT) images of each patient. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator method (LASSO) penalized Cox regression analysis. Finally, an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction. RESULTS: The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts (All P<0.05). On the multivariable analysis, independent factors for PFS were radiomics signature, performance status (PS), and N stage, which were all selected into construction of RPSS. The RPSS showed significant prognostic performance for predicting PFS in discovery [C-index: 0.772, 95% confidence interval (95% CI): 0.765-0.779], internal validation (C-index: 0.738, 95% CI: 0.730-0.746), external validation (C-index: 0.750, 95% CI: 0.734-0.765), and mutation validation (C-index: 0.739, 95% CI: 0.720-0.758). Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness (All P<0.05). CONCLUSIONS: This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stage IV NSCLC patients treated with platinum-based chemotherapy, which holds promise for guiding personalized pre-therapy of stage IV NSCLC.

Prognostic value of a modified Immunoscore in patients with stage I-III resectable colon cancer.

OBJECTIVE: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer. However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore (IS-mod) system for predicting overall survival (OS) in patients with stage I-III colon cancer. METHODS: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort (N=212) and validation cohort (N=103) from two centers were used to evaluate the prognostic value of the IS-mod. RESULTS: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS [adjusted hazard ratio (HR)=0.36, 95% confidence interval (95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like (IS-like) system (C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25% (C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort. CONCLUSIONS: Our method simplifies the annotation and accelerates the calculation of Immunoscore method, thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set (N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.

OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.

Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features.

OBJECTIVE: To evaluate the human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer using multidetector computed tomography (MDCT)-based handcrafted and deep radiomics features. METHODS: This retrospective study enrolled 339 female patients (primary cohort, n=177; validation cohort, n=162) with pathologically confirmed invasive breast cancer. Handcrafted and deep radiomics features were extracted from the MDCT images during the arterial phase. After the feature selection procedures, handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression analysis. Performance was assessed by measures of discrimination, calibration, and clinical usefulness in the primary cohort and validated in the validation cohort. RESULTS: The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739 [95% confidence interval (95% CI): 0.661-0.818] in the primary cohort and 0.695 (95% CI: 0.609-0.781) in the validation cohort. The deep radiomics signature also had a discriminative ability with a C-index of 0.760 (95% CI: 0.690-0.831) in the primary cohort and 0.777 (95% CI: 0.696-0.857) in the validation cohort. The combined model, which incorporated both the handcrafted and deep radiomics signatures, showed good discriminative ability with a C-index of 0.829 (95% CI: 0.767-0.890) in the primary cohort and 0.809 (95% CI: 0.740-0.879) in the validation cohort. CONCLUSIONS: Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast cancer. Thus, these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer.

[Effects of different wavelet filters on correlation and diagnostic performance of radiomics features].

OBJECTIVE: To investigate the effects of different wavelet filters on correlation and diagnostic performance of radiomics features.
 Methods: A total of 143 colorectal cancer (CRC) patients (64 positive in lymph node metastasis and 79 negative) with contrast-enhanced CT examination were recruited. After labeling the tumor area by experienced radiologists, radiomics wavelets features based on 48 different wavelets were extracted using in-house software coded by Matlab. The correlation coefficients of the features with same names between different wavelets were calculated and got the distribution of high-correlation features between each wavelet. The least absolute shrinkage and selection operator (LASSO) was used to build signatures between lymph node metastasis and wavelet features data set based on different wavelets. The numbers of features in signatures and diagnostic performance were compared using Delong's test.
 Results: With the difference of wavelet order increased, the number of high-correlation features between two wavelets decreased. Some features were prone to high correlation between different wavelets. When building radiomics signature based on single wavelet, signatures built from 'rbio2.2', 'sym7' and 'db7' did well in predicting lymph node metastasis. The signature based on Daubechies wavelet feature set had the highest performance in predicting lymph node metastasis, while the signature from Biorthogonal wavelet features was worst. Improvement was significant in diagnostic performance after excluding the high-correlation features in the whole features set (P=0.004).
 Conclusion: In order to reduce the data redundancy of features, it is recommended to select wavelets with large differences in wavelet orders when calculating radiomics wavelet features. It is necessary to remove high correlation features for improving the diagnostic performance of radiomics signature.

Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer.

OBJECTIVE: To develop and validate a radiomics-based predictive risk score (RPRS) for preoperative prediction of lymph node (LN) metastasis in patients with resectable non-small cell lung cancer (NSCLC). METHODS: We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016. By using the method of radiomics analysis, 591 computed tomography (CT)-based radiomics features were extracted, and the radiomics-based classifier was constructed. Then, using multivariable logistic regression analysis, a weighted score RPRS was derived to identify LN metastasis. Apparent prediction performance of RPRS was assessed with its calibration, discrimination, and clinical usefulness. RESULTS: The radiomics-based classifier was constructed, which consisted of 13 selected radiomics features. Multivariate models demonstrated that radiomics-based classifier, age group, tumor diameter, tumor location, and CT-based LN status were independent predictors. When we assigned the corresponding score to each variable, patients with RPRSs of 0-3, 4-5, 6, 7-8, and 9 had distinctly very low (0%-20%), low (21%-40%), intermediate (41%-60%), high (61%-80%), and very high (81%-100%) risks of LN involvement, respectively. The developed RPRS showed good discrimination and satisfactory calibration [C-index: 0.785, 95% confidence interval (95% CI): 0.780-0.790]. Additionally, RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index (NRI) of 0.711 (95% CI: 0.555-0.867). CONCLUSIONS: The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC. This stratification of patients according to their LN status may provide a basis for individualized treatment.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

OBJECTIVE: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). METHODS: After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. RESULTS: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). CONCLUSIONS: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Multiparametric MR diffusion-weighted imaging for monitoring the ultra-early treatment effect of sorafenib in human hepatocellular carcinoma xenografts.

PURPOSE: To investigate the value of multiparametric magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) for monitoring the ultra-early (within 24 hours) treatment effect of sorafenib in human hepatocellular carcinoma (HCC) xenografts. MATERIALS AND METHODS: With institutional Animal Care and Use Committee approval, 16 BALB/c nude mice bearing subcutaneous HCC xenografts underwent serial Gaussian and non-Gaussian DWI at baseline and 1, 3, 6, 12, and 24 hours posttreatment using a 1.5T whole-body MRI system. Gaussian-DWI-derived apparent diffusion coefficient (ADC), D, D*, and f, and non-Gaussian-DWI-derived MD, MK, DDC, and α were calculated and compared between the control (n = 6) and sorafenib-treated groups (n = 10) with respect to each timepoint using Mann-Whitney or Wilcoxon signed-rank test. Results were validated by pathology. RESULTS: Compared to baseline, ADC and D at 1 hour posttreatment (P = 0.005 and P = 0.013, respectively) and MD and DDC at 3 hours posttreatment (P = 0.009 and P = 0.005, respectively) significantly decreased and remained lower through 12 hours of follow-up (P = 0.005-0.022), followed by recovery to baseline levels at 24 hours posttreatment (P = 0.139-0.646). MK significantly increased at 1 hour posttreatment (P = 0.013) and remained higher through 24 hours of follow-up (P = 0.009-0.028). No significant differences were found in D*, f, and α at different timepoints (P = 0.188-0.714). Light microscopy did not reveal abnormal findings until 3 hours posttreatment, when central patchy necrosis was observed; more prominent diffuse necrosis was observed at 24 hours. Electron microscopy revealed swollen mitochondria at 1 hour posttreatment and accumulation of intracellular autophagosomes from 3 to 24 hours posttreatment. CONCLUSION: Multiparametric DWI might evaluate therapeutic effects of sorafenib in HCC, where metrics of ADC, D, and MK could potentially serve as imaging biomarkers for monitoring therapeutic effects as early as 1 hour after treatment. Level of Evidence 1 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2017;46:248-256.

Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer?

OBJECTIVES: To determine whether multiphasic dynamic CT can preoperatively predict lymphovascular invasion (LVI) in advanced gastric cancer (AGC). METHODS: 278 patients with AGC who underwent preoperative multiphasic dynamic CT were retrospectively recruited. Tumour CT attenuation difference between non-contrast and arterial (ΔAP), portal (ΔPP) and delayed phase (ΔDP), tumour-spleen attenuation difference in the portal phase (ΔT-S), tumour contrast enhancement ratios (CERs), tumour-to-spleen ratio (TSR) and tumour volumes were obtained. All CT-derived parameters and clinicopathological variables associated with LVI were analysed by univariate analysis, followed by multivariate and receiver operator characteristics (ROC) analysis. Associations between CT predictors for LVI and histopathological characteristics were evaluated by the chi-square test. RESULTS: ΔPP (OR, 1.056; 95% CI: 1.032-1.080) and ΔT-S (OR, 1.043; 95% CI: 1.020-1.066) are independent predictors for LVI in AGC. ΔPP, ΔT-S and their combination correctly predicted LVI in 74.8% (AUC, 0.775; sensitivity, 88.6%; specificity, 54.1%), 68.7% (AUC, 0.747; sensitivity, 68.3%; specificity, 69.4%) and 71.7% (AUC, 0.800; sensitivity, 67.6%; specificity, 77.8%), respectively. There were significant associations between CT predictors for LVI with tumour histological differentiation and Lauren classification. CONCLUSION: Multiphasic dynamic CT provides a non-invasive method to predict LVI in AGC through quantitative enhancement measurement. KEY POINTS: • Lymphovascular invasion rarely can be evaluated preoperatively in advanced gastric cancer (AGC). • Δ PP and Δ T-S were independent predictors for LVI in patients with AGC. • Δ PP and Δ T-S showed acceptable predictive performance for LVI. • Combination of Δ PP and Δ T-S improved predictive performance for LVI. • Multiphasic dynamic CT may be a useful adjunct for detecting LVI preoperatively.

Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. © RSNA, 2016 Online supplemental material is available for this article.

MRI quantification of non-Gaussian water diffusion in normal human kidney: a diffusional kurtosis imaging study.

Our aim was to prospectively evaluate the feasibility of diffusional kurtosis imaging (DKI) in normal human kidney and to report preliminary DKI measurements. Institutional review board approval and informed consent were obtained. Forty-two healthy volunteers underwent diffusion-weighted imaging (DWI) scans with a 3-T MR scanner. b values of 0, 500 and 1000 s/mm(2) were adopted. Maps of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (D⊥), axial diffusivity (D||), mean kurtosis (MK), radial kurtosis (K⊥) and axial kurtosis (K||) were produced. Three representative axial slices in the upper pole, mid-zone and lower pole were selected in the left and right kidney. On each selected slice, three regions of interest were drawn on the renal cortex and another three on the medulla. Statistical comparison was performed with t-test and analysis of variance. Thirty-seven volunteers successfully completed the scans. No statistically significant differences were observed between the left and right kidney for all metrics (p values in the cortex: FA, 0.114; MD, 0.531; D⊥, 0.576; D||, 0.691; MK, 0.934; K⊥, 0.722; K||, 0.891; p values in the medulla: FA, 0.348; MD, 0.732; D⊥, 0.470; D||, 0.289; MK, 0.959; K⊥, 0.780; K||, 0.287). Kurtosis metrics (MK, K||, K⊥) obtained in the renal medulla were significantly (p <0.001) higher than those in the cortex (0.552 ± 0.04, 0.637 ± 0.07 and 0.530 ± 0.08 in the medulla and 0.373 ± 0.04, 0.492 ± 0.06 and 0.295 ± 0.06 in the cortex, respectively). For the diffusivity measures, FA of the medulla (0.356 ± 0.03) was higher than that of the cortex (0.179 ± 0.03), whereas MD, D⊥ and D|| (mm(2) /ms) were lower in the medulla than in the cortex (3.88 ± 0.09, 3.50 ± 0.23 and 4.65 ± 0.29 in the cortex and 2.88 ± 0.11, 2.32 ± 0.20 and 3.47 ± 0.31 in the medulla, respectively). Our results indicate that DKI is feasible in the human kidney. We have reported the preliminary DKI measurements of normal human kidney that demonstrate well the non-Gaussian behavior of water diffusion, especially in the renal medulla.

Liver diffusion-weighted MR imaging: reproducibility comparison of ADC measurements obtained with multiple breath-hold, free-breathing, respiratory-triggered, and navigator-triggered techniques.

PURPOSE: To prospectively compare the reproducibility of normal liver apparent diffusion coefficient (ADC) measurements by using different respiratory motion compensation techniques with multiple breath-hold (MBH), free-breathing (FB), respiratory-triggered (RT), and navigator-triggered (NT) diffusion-weighted (DW) imaging and to compare the ADCs at different liver anatomic locations. MATERIALS AND METHODS: The study protocol was approved by the institutional review board, and written informed consent was obtained from each participant. Thirty-nine volunteers underwent liver DW imaging twice. Imaging was performed with a 1.5-T MR imager with MBH, FB, RT, and NT techniques (b = 0, 100, and 500 sec/mm(2)). Three representative sections--superior, central, and inferior--were selected on left and right liver lobes, respectively. On each selected section, three regions of interest were drawn, and ADCs were measured. Analysis of variance was used to assess ADCs among the four techniques and various anatomic locations. Reproducibility of ADCs was assessed with the Bland-Altman method. RESULTS: ADCs obtained with MBH (range: right lobe, [1.641-1.662] × 10(-3)mm(2)/sec; left lobe, [2.034-2.054] ×10(-3)mm(2)/sec) were higher than those obtained with FB (right, [1.349-1.391] ×10(-3)mm(2)/sec; left, [1.630-1.700] ×10(-3)mm(2)/sec), RT (right, [1.439-1.455] ×10(-3)mm(2)/sec; left, [1.720-1.755] ×10(-3)mm(2)/sec), or NT (right, [1.387-1.400] ×10(-3)mm(2)/sec; left, [1.661-1.736] ×10(-3)mm(2)/sec) techniques (P < .001); however, no significant difference was observed between ADCs obtained with FB, RT, and NT techniques (P = .130 to P >.99). ADCs showed a trend to decrease moving from left to right. Reproducibility in the left liver lobe was inferior to that in the right, and the central middle segment in the right lobe had the most reproducible ADC. Statistical differences in ADCs were observed in the left-right direction in the right lobe (P < .001), but they were not observed in the superior-inferior direction (P = .144-.450). However, in the left liver lobe, statistical differences existed in both directions (P = .001 to P = .016 in the left-right direction, P < .001 in the superior-inferior direction). CONCLUSION: Both anatomic location and DW imaging technique influence liver ADC measurements and their reproducibility. FB DW imaging is recommended for liver DW imaging because of its good reproducibility and shorter acquisition time compared with that of MBH, RT, and NT techniques.

Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis.

BACKGROUND: Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS: The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS: The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS: We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.