An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature.

PURPOSE: This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade. METHODS: Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The predicted values of pathological HCC grades using radiomics signatures, clinical factors (including age, sex, tumour size, alpha fetoprotein (AFP) level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) and the combined models were assessed. RESULTS: Radiomics signatures could successfully categorise high-grade and low-grade HCC cases (p < 0.05) in both the training and test datasets. Regarding the performances of clinical factors, radiomics signatures and the combined clinical and radiomics signature (from the combined T1WI and T2WI images) models for HCC grading prediction, the areas under the curve (AUCs) were 0.600, 0.742 and 0.800 in the test datasets, respectively. Both the AFP level and radiomics signature were independent predictors of HCC grade (p < 0.05). CONCLUSIONS: Radiomics signatures may be important for discriminating high-grade and low-grade HCC cases. The combination of the radiomics signatures with clinical factors may be helpful for the preoperative prediction of HCC grade. KEY POINTS: • The radiomics signature based on non-contrast-enhanced MR images was significantly associated with the pathological grade of HCC. • The radiomics signatures based on T1WI or T2WI images performed similarly at predicting the pathological grade of HCC. • Combining the radiomics signature and clinical factors (including age, sex, tumour size, AFP level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) may be helpful for the preoperative prediction of HCC grade.

[Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images].

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.

Radiologic and Clinicopathologic Findings of Inflammatory Myofibroblastic Tumor.

PURPOSE: The aim of the study was to describe the clinical, radiographic, and pathologic features of inflammatory myofibroblastic tumor (IMT) to enhance the recognition of this rare disease. MATERIALS AND METHODS: The clinical, imaging, and pathologic findings were retrospectively reviewed in 54 patients with IMT lesions, which were conformed by biopsy or surgical pathology. Of 54 patients, 51 had preoperative computed tomography (CT) examination and 13 had preoperative magnetic resonance imaging records. RESULTS: The clinical appearances of these 54 patients had some relationship with the locations of lesions. Of 54 IMT patients, 87.0% cases (47/54) had solitary lesion. The mean long diameter of the lesions located at the sites of chest, abdomen, and pelvic regions was bigger than that of other locations (F = 3.025, P = 0.038). On plain CT images, soft tissue mass was found in all IMT lesions, except for 3 lesions that arose in the intestine tract, appearing as focal or diffuse thickening in the bowel wall. After contrast administration, all lesions were persistently enhanced; 72.7% cases (24/33) demonstrated heterogeneous enhancement with various cystic regions. Comparing the CT features with different anatomic lesions, ill-defined margin on the plain CT images and calcification were seen more frequently in the lesions of the head and neck (P = 0.010 and 0.035); however, the other radiological findings had no significant differences (all P > 0.05). Twelve of 51 IMT patients showed invasion into adjacent structures. On magnetic resonance imaging, 92.3% lesions (12/13) showed soft tissue masses demonstrating isointense to hypointense contrast compared with skeletal muscle on T1-weighted images and heterogeneously high signals on T2-weighted images; 85.7%(6/7) of lesions were heterogeneously enhanced with cystic changes. Immunohistochemistry showed that the percentage of positive staining for SMA, vimentin, anaplastic lymphoma kinase, CD68, CD34, CD99, B-cell lymphoma/leukemia-2, cytokeratin, Desmin, and S-100 protein were 88.9%, 87.0%, 44.4%, 59.3%, 53.7%, 29.6%, 42.6%, 28.5%, 13.0%, and 24.1%, respectively. CONCLUSIONS: Inflammatory myofibroblastic tumor can involve any part of the body, and the clinical and radiological appearances are various owing to different anatomic sites. An ill-defined soft tissue mass heterogeneous enhancement with or without invasion into adjacent structures on computed tomographic or magnetic resonance images and positive staining for SMA and vimentin on immunohistochemical examination could suggest the diagnosis.

A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules.

BACKGROUND: Lung cancer is the most common cancer which has the highest mortality rate. With the development of computed tomography (CT) techniques, the case detection rates of solitary pulmonary nodules (SPN) has constantly increased and the diagnosis accuracy of SPN has remained a hot topic in clinical and imaging diagnosis. The aim of this study was to evaluate the combination of low-dose spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm in the diagnosis of solitary pulmonary nodules (SPN). METHODS: 62 patients with SPN (42 cases of benign SPN and 20 cases of malignant SPN, pathology confirmed) were scanned by spectral CT with a dual-phase contrast-enhanced method. The iodine and water concentration (IC and WC) of the lesion and the artery in the image that had the same density were measured by the GSI (Gemstone Spectral Imaging) software. The normalized iodine and water concentration (NIC and NWC) of the lesion and the normalized iodine and water concentration difference (ICD and WCD) between the arterial and venous phases (AP and VP) were also calculated. The spectral HU (Hounsfield Unit ) curve was divided into 3 sections based on the energy (40-70, 70-100 and 100-140 keV) and the slopes (λHU) in both phases were calculated. The ICAP, ICVP, WCAP and WCVP, NIC and NWC, and the λHU in benign and malignant SPN were compared by independent sample t-test. RESULTS: The iodine related parameters (ICAP, ICVP, NICAP, NICVP, and the ICD) of malignant SPN were significantly higher than that of benign SPN (t = 3.310, 1.330, 2.388, 1.669 and 3.251, respectively, P <0.05). The 3 λHU values of venous phase in malignant SPN were higher than that of benign SPN (t = 3.803, 2.846 and 3.205, P <0.05). The difference of water related parameters (WCAP, WCVP, NWCAP, NWCVP and WCD) between malignant and benign SPN were not significant (t = 0.666, 0.257, 0.104, 0.550 and 0.585, P > 0.05). CONCLUSIONS: The iodine related parameters and the slope of spectral curve are useful markers to distinguish the benign from the malignant lung diseases, and its application is extremely feasible in clinical applications.

Radiologic and clinicopathologic findings of peripheral primitive neuroectodermal tumors.

BACKGROUND: Primitive neuroectodermal tumors (PNETs) constitute a rare type of malignant neuroectodermal tumors that have chromosomal translocations identical to Ewing's sarcoma (ES), and the characteristics of this disease remain unclear. PURPOSE: To describe the clinical, radiological, and pathological features of peripheral PNETs (pPNETs) to enhance their recognition. MATERIAL AND METHODS: The clinical, imaging, and pathologic findings of 35 patients with pPNETs were retrospectively reviewed, all being confirmed by biopsy or surgical pathology. All 35 patients had preoperative computed tomography (CT) examinations; 10 patients had preoperative magnetic resonance imaging (MRI) studies. RESULTS: Of 35 pPNET patients, 54.3% had a primary tumor in soft tissue, the others in bone. On plain CT images, 33 lesions demonstrated heterogeneous hypodense masses with multiple lamellar lower density, and with osteolytic destruction if the tumor originated in bone. Calcification was only found in five lesions arising in soft tissue. All lesions enhanced heterogeneously with varying areas of cystic changes, and all lesions in bone and 52.6% of lesions in soft tissue showed ill-defined margins after contrast administration. On MRI, these tumors appeared in conjunction with osteolytic bone destruction and irregular soft tissue masses iso- to hypointense to skeletal muscle on T1-weighted images and showed heterogeneously high intensity on T2-weighted images. All lesions enhanced heterogeneously with cystic changes. Homer-Wright rosettes were observed in 15 lesions, and 97.1% lesions were positive for CD99 in histopathological results. CONCLUSION: pPNETs can involve any part of the body, and a large, ill-defined, aggressive soft tissue mass and heterogeneous enhancement with or without osteolytic bone destruction on CT or MR images could suggest the diagnosis.

[The best noise index combined with ASIR weighting selection in low-dose chest scanning].

OBJECTIVE: To discuss the best noise index combined with ASIR weighting selection in low-dose chest scanning based on BMI. METHODS: 200 patients collected from May to December 2014 underwent non-contrast chest CT examinations, they were randomly assigned into standard dose group (Group A, NI15 combined with 30% ASIR) and low-dose groups (Group B, NI25 combined with 40% ASIR, Group C, NI30 combined with 50% ASIR, Group D, NI35 combined with 60% ASIR), 50 cases in each group; the patients were assigned into three groups based on BMI (kg/m2): BMI<18.5; 18.5≤BMI≤25; BMI>25. Signal-to-nosie ratio (SNR), contrast-to noise ratio (CNR), CT dose index volume (CTDIvol), dose-length product (DLP), effective dose (ED) and subjective scoring between the standard and low-dose groups were compared and analyzed statistically. Differences of SNR, CNR, CTDIvol, DLP and ED among groups were determined with ANOVA analysis and the consistency of diagnosis with Kappa test. RESULTS: SNR, CTDIvol, DLP and ED reduced with the increase of nosie index, the differences among the groups were statistically significant (P<0.05). Kappa value of the two reviewers were 0.888. Subjective scoring of four groups were all above 3 points in BMI<18.5 kg/m2 group; subjective scoring of ABC groups were all above 3 points in 18.5 kg/m2≤BMI≤25 kg/m2 group and subjective scoring of AB groups were all above 3 points in BMI>25 kg/m2 group. CONCLUSIONS: NI35 combined with 60% ASIR in BMI<18.5 kg/m2 group; NI30 combined with 50% ASIR in 18.5 kg/m2≤BMI≤25 kg/m2 group; NI25 combined with 40% ASIR in 18.5 kg/m2≤BMI≤25 kg/m2 group were the best parameters combination which both can significantly reduce the radiation dose and ensure the image quality.

Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study.

Purpose: To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method: In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROIIntra) was dilated by 2, 4, 6 and 8 mm (ROIPeri2mm, ROIPeri4mm, ROIPeri6mm, and ROIPeri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat-saturated T2-weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. Result: The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648-0.778), 0.706 (95% CI, 0.637-0.768), and 0.719 (95% CI, 0.651-0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686-0.810), 0.754 (95% CI, 0.688-0.812) and 0.725 (95% CI, 0.657-0.786), respectively, in the test set. Combining three sequences in the ROIIntra, ROIPeri2mm, ROIPeri4mm, ROIPeri6mm and ROIPeri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733-0.849) in the test set. Conclusion: This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.

Objective: To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods: A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results: A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion: The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Localization and evaluation of sentinel lymph node in breast cancer from computed tomographic lymphography.

OBJECTIVE: This study aimed to determine the accuracy of computed tomographic (CT) localization and CT-based diagnosis of sentinel lymph nodes (SLNs) metastasis. METHODS: Thirty-four patients with confirmed breast cancer underwent 40-row CT scanning, and the first one or several lymph node(s) in the lymphatic drainage pathway was/were defined as the SLN(s). Dye and γ probe-guided SLN biopsy was performed on all patients. To accurately localize the SLN, 19 patients (55.9%) underwent the percutaneous lymph node puncture procedure. The morphologic features of all the SLNs on CT scans were analyzed and compared with the SLN biopsy pathologic diagnosis. RESULTS: Sentinel lymph nodes were successfully identified for all patients without any significant adverse effects. All localized SLNs corresponded well with SLNs identified on SLN biopsy, with an accuracy of 89.5%. Accuracy increased to 100% when the CT scan technique was combined with the blue dye method. The size criteria for metastatic diagnosis had a sensitivity of 85%, which increased to 94.7% when long-to-short-axis ratio and margin characteristics were also considered. CONCLUSIONS: The CT lymphography combined with the blue dye method accurately localized the SLNs. The CT-based diagnostic criteria improved the diagnostic accuracy of SLN metastases and were useful for evaluating the axillary status in early stage breast cancer patients.