Purpose
Preoperative prediction of perineural invasion (PNI) and Kirsten RAS (KRAS) mutation in colon cancer is critical for treatment planning and patient management. We developed machine learning ...models for diagnosis of PNI and KRAS mutation in colon cancer patients by interpreting preoperative CT.
Methods
This retrospective study included 207 patients who received surgical resection in our institution. The underlying tumor characteristics were described by analyzing CT image texture quantitatively. The key radiomics features were determined with similarity analysis followed by RELIEFF method among 306 CT imaging features. Eight kernel-based support vector machines classifiers were constructed using individual (II, III, or IV) or multi-stage (II + III + IV) patient cohorts for predicting PNI and KRAS mutation. The model performances were evaluated using accuracy, receiver operating curve, and decision curve analyses.
Results
Multi-stage classifiers obtained AUC of 0.793 and 0.862 for detecting PNI and KRAS mutation for test cohort. Moreover, individual-stage classifiers demonstrated significantly improved diagnostic performance at all stages (II
AUC
: 0.86; 0.99, III
AUC
: 0.99; 0.99, and IV
AUC
: 1.00; 1.00, respectively, for PNI and KRAS mutation in test cohort). Besides, stage II tumor is better described with coarse texture features while more detailed features are required for better characterization of advanced-stage tumors (III and IV) for diagnoses of PNI or KRAS mutation.
Conclusion
Machine learning models developed using preoperative CT data can predict PNI and KRAS mutation in colon cancer patients with satisfactory performance. Individual-stage models better-characterized the relationship between CT features and PNI or KRAS mutation than multi-stage models and demonstrated good prediction scores.
Background
Late gadolinium enhancement (LGE) cardiac MRI is the method of choice in revealing the presence of myocardial scarring, but its availability remains limited in clinical practice.
Purpose
...To assess myocardial scarring in patients with autoimmune rheumatic diseases (ARDs) using contrast‐free cardiac MRI with a radiomics model.
Study Type
Retrospective.
Population
One hundred ninety‐two patients (mean age, 41 years ± 15, 62 men) with or without ARDs, grouped into a training set of 153 patients and a testing set of 39 patients.
Field Strength/Sequence
3.0 T/ cine imaging with a balanced steady‐state free precession sequence, T1 mapping with a modified Look‐Locker inversion recovery sequence, and LGE imaging with a phase‐sensitive inversion recovery gradient echo sequence.
Assessment
LGE assessment was the reference standard for identifying myocardial scarring. Based on motion features extracted from cine images and tissue characterization features extracted from native T1 maps, a fully automated radiomics model with T1, cine MRI, or combined inputs was developed.
Statistical Tests
Logistic regression model was used to detect myocardial scarring using contrast‐free cardiac MRI parameters. Receiver operating characteristic curves were analyzed to assess the accuracy, sensitivity, and specificity in detecting myocardial scarring. Sensitivities of the models were further assessed in patients with various myocardial scarring proportions. Z‐statistic and dice coefficient were assessed to compare the performance. P‐values <0.05 were considered significant.
Results
The multivariable regression model exhibited an accuracy of 85.3%, a sensitivity of 93.5%, and a specificity of 50.0%. The radiomics model with T1 and cine MRI input exhibited an accuracy of 75.7%, a sensitivity of 60.9%, and a specificity of 85.5%. Moreover, the radiomics model showed a sensitivity of 90.9% among patients with >25% myocardial scarring.
Data Conclusions
The proposed radiomics model allowed for the identification of myocardial scarring similar to LGE, but on contrast‐free cardiac MRI in patients with ARDs.
Evidence Level
3
Technical Efficacy
Stage 1
There is a great demand for accurate non‐invasive measures to better define the natural history of disease progression or treatment outcome in Duchenne muscular dystrophy (DMD) and to facilitate the ...inclusion of a large range of participants in DMD clinical trials. This review aims to investigate which MRI sequences and analysis methods have been used and to identify future needs. Medline, Embase, Scopus, Web of Science, Inspec, and Compendex databases were searched up to 2 November 2019, using keywords “magnetic resonance imaging” and “Duchenne muscular dystrophy.” The review showed the trend of using T1w and T2w MRI images for semi‐qualitative inspection of structural alterations of DMD muscle using a diversity of grading scales, with increasing use of T2map, Dixon, and MR spectroscopy (MRS). High‐field (>3T) MRI dominated the studies with animal models. The quantitative MRI techniques have allowed a more precise estimation of local or generalized disease severity. Longitudinal studies assessing the effect of an intervention have also become more prominent, in both clinical and animal model subjects. Quality assessment of the included longitudinal studies was performed using the Newcastle‐Ottawa Quality Assessment Scale adapted to comprise bias in selection, comparability, exposure, and outcome. Additional large clinical trials are needed to consolidate research using MRI as a biomarker in DMD and to validate findings against established gold standards. This future work should use a multiparametric and quantitative MRI acquisition protocol, assess the repeatability of measurements, and correlate findings to histologic parameters.
Pancreatic ductal adenocarcinoma (PDAC) is associated with highly immunosuppressive tumor microenvironment (TME) that can limit the efficacy of dendritic cell (DC) vaccine immunotherapy. Irreversible ...electroporation (IRE) is a local ablation approach. Herein, we test the hypothesis that IRE ablation can overcome TME immunosuppression to improve the efficacy of DC vaccination using Kras
LSL-G12D
-p53
LSL-R172H
-Pdx-1-Cre (KPC) orthotopic mouse model of PDAC. The median survival for mice treated with the combined IRE and DC vaccination was 77 days compared with sham control (35 days), DC vaccination (49 days), and IRE (44 days) groups (P = .006). Thirty-six percent of the mice treated with combination IRE and DC vaccination were still survival at the end of the study period (90 days) without visible tumor. The changes of tumor apparent diffusion coefficient (ΔADC) were higher in mice treated with combination IRE and DC vaccination than that of other groups (all P < .001); tumor ΔADC value positively correlated with tumor fibrosis fraction (R = 0.707, P < .001). IRE induced immunogenic cell death and alleviation of immunosuppressive components in PDAC TME when combined with DC vaccination, including increased tumor infiltration of CD8
+
T cells and Granzyme B
+
cells (P = .001, and P = .007, respectively). Our data show that IRE ablation can overcome TME immunosuppression to improve the efficacy of DC vaccination in PDAC. Combination IRE ablation and DC vaccination may enhance therapeutic efficacy for PDAC.
Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising ...approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations.
Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient.
A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94).
Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes.
Despite significant advances over the past decades of research, pancreatic cancer (PC) continues to have the worst 5-year survival of any malignancy. Dendritic cells (DCs) are the most potent ...professional antigen-presenting cells and are involved in the induction and regulation of antitumor immune responses. DC-based immunotherapy has been used in clinical trials for PC. Although safety, efficacy, and immune activation were reported in patients with PC, DC vaccines have not yet fulfilled their promise. Additional strategies for combinatorial approaches aimed to augment and sustain the antitumor specific immune response elicited by DC vaccines are currently being investigated. Here, we will discuss DC vaccination immunotherapies that are currently under preclinical and clinical investigation and potential combination approaches for treating and improving the survival of PC patients.
Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to ...determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.
A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).
The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.
The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.
Objective: Histology is often used as a gold standard to evaluate noninvasive imaging modalities such as a magnetic resonance imaging (MRI). Spatial correspondence between histology and MRI is a ...critical step in quantitative evaluation of skeletal muscle in golden retriever muscular dystrophy (GRMD). Registration becomes technically challenging due to nonorthogonal histology section orientation, section distortion, and the different image contrast and resolution. Methods: This study describes a three-step procedure to register histology images with multiparametric MRI, i.e., interactive slice localization controlled by a three-dimensional mouse, followed by an affine transformation refinement, and a B-spline deformable registration using a new similarity metric. This metric combines mutual information and gradient information. Results: The methodology was verified using ex vivo high-resolution multiparametric MRI with a resolution of 117.19 μm (i.e., T1-weighted and T2-weighted MRI images) and trichrome stained histology images acquired from the pectineus muscles of ten dogs (nine GRMD and one healthy control). The proposed registration method yielded a root mean squares (RMS) error of 148.83 ± 34.96 μm averaged for ten muscle samples based on landmark points validated by five observers. The best RMS error averaged for ten muscles, was 128.48 ± 25.39 μm. Conclusion: The established correspondence between histology and in vivo MRI enables accurate extraction of MRI characteristics for histologically confirmed regions (e.g., muscle, fibrosis, and fat). Significance: The proposed methodology allows creation of a database of spatially registered multiparametric MRI and histology. This database will facilitate accurate monitoring of disease progression and assess treatment effects noninvasively.
Introduction
Golden retriever muscular dystrophy (GRMD) is a spontaneous X‐linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is ...proposed as an imaging biomarker of disease severity in GRMD.
Methods
To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small‐bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray‐level co‐occurrence matrix, gray‐level run‐length matrix), and a gradient descriptor (histogram of oriented gradients).
Results
The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave‐one‐out cross‐validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs.
Discussion
MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.
PDF
