Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present ...understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (
= 353) and verified them in an independent validation cohort (
= 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter). We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR
and EGFR
cases (AUC = 0.69). Combining this signature with a clinical model of EGFR status (AUC = 0.70) significantly improved prediction accuracy (AUC = 0.75). The highest performing signature was capable of distinguishing between EGFR
and KRAS
tumors (AUC = 0.80) and, when combined with a clinical model (AUC = 0.81), substantially improved its performance (AUC = 0.86). A KRAS
/KRAS
radiomic signature also showed significant albeit lower performance (AUC = 0.63) and did not improve the accuracy of a clinical predictor of KRAS status. Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.
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Background
The role of apparent diffusion coefficient (ADC)‐based radiomics features in evaluating histopathological grade of cervical cancer is unresolved.
Purpose
To determine if there is a ...difference between radiomics features derived from center‐slice 2D versus whole‐tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications.
Study Type
Prospective.
Subjects
In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix.
Field Strength/Sequence
Conventional and diffusion‐weighted MR images (b values = 0, 800, 1000 s/mm2) were acquired on a 3.0T MR scanner.
Assessment
Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole‐tumor segmentation. A total of 624 radiomics features were derived from T2‐weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis.
Statistical Tests
Parameters were compared using Wilcoxon signed rank test, Bland–Altman analysis, t‐test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation.
Results
In all, 95 radiomics features were insensitive to ROI variation among T2 images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center‐slice and 3D whole‐tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076).
Data Conclusion
Several radiomics features extracted from T2 images and ADC maps were highly reproducible. Whole‐tumor volumetric 3D radiomics analysis had a better performance than using the 2D center‐slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm2 is suggested as the optimal parameter in pelvic DWI scans.
Level of Evidence: 1
Technical Efficacy: Stage 1
J. Magn. Reson. Imaging 2019;49:280–290.
Objective
To compare the classification based on contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) with that of contrast-enhanced CT and MRI (CECT/MRI) LI-RADS for ...liver nodules in patients at high risk of hepatocellular carcinoma.
Methods
Two hundred thirty-nine patients with 273 nodules were enrolled in this retrospective study. Each nodule was categorized according to the CEUS LI-RADS version 2017 and CECT/MRI LI-RADS version 2017. The diagnostic performance of CEUS and CECT/MRI was compared. The reference standard was histopathology diagnosis. Inter-modality agreement was assessed with Cohen’s kappa.
Results
The inter-modality agreement for CEUS LI-RADS and CECT/MRI LI-RADS was fair with a kappa value of 0.319 (
p
< 0.001). The positive predictive values (PPVs) of hepatocellular carcinoma (HCC) in LR-5, LR-4, and LR-3 were 98.3%, 60.0%, and 25.0% in CEUS, and 95.9%, 65.7%, and 48.1% in CECT/MRI, respectively. The sensitivities and specificities of LR-5 for diagnosing HCC were 75.6% and 93.8% in CEUS, and 83.6% and 83.3% in CECT/MRI, respectively. The positive predictive values of non-HCC malignancy in CEUS LR-M and CECT/MRI LR-M were 33.9% and 93.3%, respectively. The sensitivity, specificity, and accuracy for diagnosing non-HCC malignancy were 90.9%, 84.5%, and 85.0% in CEUS LR-M and 63.6%, 99.6%, and 96.7% in CECT/MRI LR-M, respectively.
Conclusions
The inter-modality agreement of the LI-RADS category between CEUS and CECT/MRI is fair. The positive predictive values of HCCs in LR-5 of the CEUS and CECT/MRI LI-RADS are comparable. CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M.
Key Points
• The inter-modality agreement for the final LI-RADS category between CEUS and CECT/MRI is fair.
• The LR-5 of CEUS and CECT/MRI LI-RADS corresponds to comparable positive predictive values (PPVs) of HCC. For LR-3 and LR-4 nodules categorized by CECT/MRI, CEUS examination should be performed, at least if they can be detected on plain ultrasound.
• CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M. For LR-M nodules categorized by CEUS, re-evaluation by CECT/MRI is necessary.
Primary pericardial angiosarcoma is a rare malignant cardiac neoplasm with early metastasis and poor prognosis. There are currently no guidelines or effective therapeutic strategies. Here we report a ...case of a 22-year-old man who presented with chest pain, suffocation and transient syncope over the course of 4 months. Further workup showed a large mass in the right pericardium, histopathologic examination revealed angiosarcoma. The patient subsequently received a total of 8 cycles of chemotherapy (paclitaxel and doxorubicin). This patient has an overall survival of 1 year to date. The current examination methods and reported cases revealed that early detection of primary pericardial angiosarcoma with imaging examinations is critical for prognosis.
Dedicated breast CT is being increasingly used for breast imaging. This technique provides images with no compression, removal of tissue overlap, rapid acquisition, and available simultaneous ...assessment of microcalcifications and contrast enhancement. In this second installment in a 2-part review, the current status of clinical applications and ongoing efforts to develop new imaging systems are discussed, with particular emphasis on how to achieve optimized practice including lesion detection and characterization, response to therapy monitoring, density assessment, intervention, and implant evaluation. The potential for future screening with breast CT is also addressed.
Key Points
• Dedicated breast CT is an emerging modality with enormous potential in the future of breast imaging by addressing numerous clinical needs from diagnosis to treatment.
• Breast CT shows either noninferiority or superiority with mammography and numerical comparability to MRI after contrast administration in diagnostic statistics, demonstrates excellent performance in lesion characterization, density assessment, and intervention, and exhibits promise in implant evaluation, while potential application to breast cancer screening is still controversial.
• New imaging modalities such as phase-contrast breast CT, spectral breast CT, and hybrid imaging are in the progress of R & D.
Dedicated breast CT is an emerging 3D isotropic imaging technology for breast, which overcomes the limitations of 2D compression mammography and limited angle tomosynthesis while providing some of ...the advantages of magnetic resonance imaging. This first installment in a 2-part review describes the evolution of dedicated breast CT beginning with a historical perspective and progressing to the present day. Moreover, it provides an overview of state-of-the-art technology. Particular emphasis is placed on technical limitations in scan protocol, radiation dose, breast coverage, patient comfort, and image artifact. Proposed methods of how to address these technical challenges are also discussed.
Key Points
• Advantages of breast CT include no tissue overlap, improved patient comfort, rapid acquisition, and concurrent assessment of microcalcifications and contrast enhancement.
• Current clinical and prototype dedicated breast CT systems differ in acquisition modes, imaging techniques, and detector types.
• There are still details to be decided regarding breast CT techniques, such as scan protocol, radiation dose, breast coverage, patient comfort, and image artifact.
Purpose
To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from
Mycobacterium ...tuberculosis
lung disease (MTB-LD).
Method
Chest CT images of 301 with NTM-LD and 804 with MTB-LD confirmed by pathogenic microbiological examination were retrospectively collected. The differences between the clinical manifestations of the two diseases were analysed. 3D-ResNet was developed to randomly extract data in an 8:1:1 ratio for training, validating, and testing. We also collected external test data (40 with NTM-LD and 40 with MTB-LD) for external validation of the model. The activated region of interest was evaluated using a class activation map. The model was compared with three radiologists in the test set.
Result
Patients with NTM-LD were older than those with MTB-LD, patients with MTB-LD had more cough, and those with NTM-LD had more dyspnoea, and the results were statistically significant (
p
< 0.05). The AUCs of our model on training, validating, and testing datasets were 0.90, 0.88, and 0.86, respectively, while the AUC on the external test set was 0.78. Additionally, the performance of the model was higher than that of the radiologist, and without manual labelling, the model automatically identified lung areas with abnormalities on CT > 1000 times more effectively than the radiologists.
Conclusion
This study shows the efficacy of 3D-ResNet as a rapid auxiliary diagnostic tool for NTB-LD and MTB-LD. Its use can help provide timely and accurate treatment strategies to patients with these diseases.
Abstract Purpose To investigate whether dual energy computed tomography (CT) with iodine quantification can characterize primary lesions and metastatic lymph nodes from non-metastatic ones in ...non-small cell lung cancer (NSCLC). Materials and Methods Sixty-one patients with NSCLC confirmed by pathology underwent chest contrast CT scan with dual energy computed tomography before surgery. The Iodine concentration (IC) and normalised iodine concentration (NIC) values of the primary lesions, 20 metastatic and 20 non-metastatic lymph nodes were measured, respectively. The differences between the primary lesions, metastatic and non-metastatic lymph nodes were statistically analyzed. Results For the IC and NIC values of the primary lesions and their metastatic lymph nodes, there were no significant differences between lung squamous cell carcinomas and adenocarcinomas, respectively ( P > 0.05), while significant differences existed between metastatic and non-metastatic lymph nodes, respectively ( P < 0.05). The IC of 29.32 100 μg/cm3 and NIC value of 0.4328 of a lymph node represented the optimal threshold to discriminate metastatic from non-metastatic lymph nodes and yielded the following: sensitivity, 80% and 75%; specificity, 65% and 75%; PPV, 70% and 75%; NPV, 76% and 75%; accuracy, 73% and 75%, respectively. Conclusion Although its value in distinguishing primary lesions and their metastatic lymph nodes in NSCLC needs to be verified in further studies, dual energy CT with iodine quantification may be used to differentiate metastatic from non-metastatic lymph nodes in NSCLC.
•Radiomics model has good predictive ability for nodal metastasis in gastric cancer•Radiomics model is superior to routine CT scan•Radiomics model helps define high-risk of nodal metastasis in early ...stage patients•Radiomics model may facilitate clinical decision-making
To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC).
A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup.
The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort AUC, 0.92; 95% confidence interval (CI), 0.89-0.95 and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model.
The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.
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