Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics.

OBJECTIVES: To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics. METHODS: One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models. RESULTS: The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model. CONCLUSIONS: Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics. CLINICAL RELEVANCE STATEMENT: This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance. KEY POINTS: •Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.

Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma.

OBJECTIVES: To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma. METHODS: Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without. RESULTS: PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit. CONCLUSION: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. CRITICAL RELEVANCE STATEMENT: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.

The Serious Challenge of Occult Hepatitis B Virus Infection-Related Hepatocellular Carcinoma in China.

BACKGROUND: It is unknown how many people in China have chronic occult hepatitis B virus (HBV) infection (OBI) [chronic HBV infection with negative serum hepatitis B surface antigen (HBsAg) (N-HBsAg)]. Their clinical and virological characteristics, especially the correlation between the OBI and hepatocellular carcinoma (HCC), are still elusive and need to be investigated, including prevention, early diagnosis, and treatment strategies. METHODS: 138 patients with HCC related to OBI were screened from 698 patients of HCC associated with HBV infection, their characteristics of epidemiology, clinical, biochemistry, virology, diagnostics, and therapeutics were analyzed retrospectively. Furthermore, the correlation between virological features and clinical features was investigated. RESULTS: It was found that 19.8% (138/698) of patients with HBV-related HCC were OBI, of which 79.7% (110/138) were men, and 20.3% (28/138) were women. Most of the patients with OBI-related HCC were older men, and the median age was 63.2 years. In total 78.3% (108/138) of the patients had apparent right upper abdomen discomfort and/or pain and then sought medical examination, while 21.7% (30/138) of the patients were identified by health examination. A total of 10.9% (15/138) of the patients were admitted with chronic infection of HBV, and 2.2% (3/138) of the patients were admitted with a family history of hepatitis B. The alpha-fetoprotein (AFP) serum-positive rate was 39.1% (54/138). Tumor lesions >5.0 cm, with intrahepatic and/or extrahepatic metastasis, were found in 72.5% (100/138) of the patients. The diameter of the tumor in the Group of hepatitis B core antibody-positive [HBcAb(+)] and hepatitis B surface antibody-positive [HBsAb(+)] was 7.03 ± 3.76 cm, which was much smaller than 8.79 ± 4.96 cm in the Group of HBcAb(+) and HBsAb(-) (P = 0.035). CONCLUSION: It is estimated that at least 21 million OBI patients live in China. HBcAb(+) was not only the evidence of chronic HBV infection but also a dangerous mark for surface antigen-negative patients. A semi-annual or annual medical checkup is essential for all OBI patients to identify HCC as early as possible. The hypothesis underlying our analysis was that hepatitis B surface antibody would prevent the progress of HCC and facilitate the clearance of HBV in patients with OBI. Thereby, the hepatitis B vaccine could be used to prevent severe disease consequences.

The Investigation of Hepatitis B Vaccine Immune Responses in Occult Hepatitis B Virus-Infected Patients.

Objectives: There is no effective treatment for occult hepatitis B virus infection (OBI) patients, and immunotherapy may be one of the most promising options. We aim to investigate the underlying mechanism and therapeutic potential of hepatitis B vaccine immunotherapy for OBI patients. Methods: Outpatient OBI patients were screened and randomly divided into treatment (Group A) and control (Group B) groups. At weeks 0, 4, and 24, patients in Group A received a subcutaneous/intramuscular injection of hepatitis B vaccine (Engerix-B, 20 µg/time) according to the standard vaccination schedule; patients in Group B served as blank control. The patients were followed for 36 weeks, with clinical, biochemical, virological, immunological, and imaging data collected and analyzed at weeks 0, 12, 24, and 36, respectively, and the relation between the virology and immunology results was analyzed. Results: Of the 228 OBI patients, 28 were excluded, and 200 were enrolled for observation. In the end, 44 patients were included in Group A and 39 in Group B after excluding lost cases. At week 0 (baseline), some patients in two groups had liver disease symptoms, HBV-related liver function damage, and liver fibrosis. 86.36% (38/44) and 82.05% (32/39) patients were positive for serum hepatitis B surface antibodies (anti-HBs) in Group A and Group B, respectively, with the median (quartile) of 42.47 (16.85, 109.1) and 39.27 (16.06, 117.4) mIU/ml, respectively. Reduced peripheral blood CD4+T, CD8+T, and B lymphocytes were found in some patients in two groups. These results were not statistically different between Group A and Group B (P>0.05). At week 36, all patients were serum anti-HBs (+) in Group A, with a median (quartile) of 1000 (483.9, 1000) mIU/ml, which was significantly higher than that at week 0 (P<0.05) and that in Group B (P<0.05). Compared to week 0, the number of CD8+ T and B lymphocytes increased significantly and were significantly higher than Group B at the same point. Two patients in Group B were found to have hepatitis B virus reactivation from week 12 to week 36. Correlation Analysis: Anti-HBs in Group A patients were positively correlated with B lymphocytes (r=0.3431, 0.3087, and 0.3041, respectively) and positively correlated with CD8+ T lymphocytes (r=0.4954, 0.3054, and 0.3455, respectively) at weeks 12, 24, and 36. Conclusion: Virological reactivation is a risk for OBI patients. Serum hepatitis B surface antibodies were significantly increased after hepatitis B vaccine treatment, the same as the numbers of peripheral blood B and CD8+ T lymphocytes; changes in hepatitis B surface antibody levels were positively correlated with the changes in peripheral blood B and CD8+ T lymphocytes.

Reactivation of Occult Hepatitis B Virus Infection During Long-Term Entecavir Antiviral Therapy.

Up to now, it has not been clear whether occult hepatitis B virus (HBV) infection (OBI) can be treated with antiviral therapy whether OBI can develop drug resistance gene mutation or not. We report a middle-aged female patient with OBI who showed HBV reactivation (HBVr) during more than 3 years of intermittent entecavir (ETV) antiviral therapy: seropositive HBV surface antigen (HBsAg), increased e antigen (HBeAg), and repeatedly elevated serum HBV DNA. Genotype analysis showed that the patient was infected with HBV type B. Genetic sequencing of HBV showed the mutants of S143T, D144G, and G145R in the S gene region, and the mutant of site 1896 in the pre-Core region coexisted with the wild type (G1896A/G). No mutation was found in other HBV gene segments. Drug resistance gene analysis found RtL229W mutant, resistant to lamivudine but sensitive to ETV and other nucleoside analogs. This case of OBI provides us with the following clinical experiences: Firstly, it is necessary to detect HBV genotype, mutation, and drug-resistant genes at the initial diagnosis, which can be helpful for reasonable treatment. Secondly, identifying the risk factors and mechanisms associated with HBVr could help quantify the risk of HBVr and manage the clinical consequences. Thirdly, the OBI patients with hepatitis B e antigen-positive, HBV DNA > 1 × 103 IU/ml should be recommended regular and continuous antiviral therapy as soon as possible to prevent the occurrence of hepatocirrhosis and hepatocellular carcinoma (HCC).

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study.

Background: The tumor immune microenvironment (TIME) phenotypes have been reported to mainly impact the efficacy of immunotherapy. Given the increasing use of immunotherapy in cancers, knowing an individual's TIME phenotypes could be helpful in screening patients who are more likely to respond to immunotherapy. Our study intended to establish, validate, and apply a machine learning model to predict TIME profiles in non-small cell lung cancer (NSCLC) by using 18F-FDG PET/CT radiomics and clinical characteristics. Methods: The RNA-seq data of 1145 NSCLC patients from The Cancer Genome Atlas (TCGA) cohort were analyzed. Then, 221 NSCLC patients from Daping Hospital (DPH) cohort received18F-FDG PET/CT scans before treatment and CD8 expression of the tumor samples were tested. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images and develop a radiomics signature. The models were established by radiomics, clinical features, and radiomics-clinical combination, respectively, the performance of which was calculated by receiver operating curves (ROCs) and compared by DeLong test. Moreover, based on radiomics score (Rad-score) and clinical features, a nomogram was established. Finally, we applied the combined model to evaluate TIME phenotypes of NSCLC patients in The Cancer Imaging Archive (TCIA) cohort (n = 39). Results: TCGA data showed CD8 expression could represent the TIME profiles in NSCLC. In DPH cohort, PET/CT radiomics model outperformed CT model (AUC: 0.907 vs. 0.861, P = 0.0314) to predict CD8 expression. Further, PET/CT radiomics-clinical combined model (AUC = 0.932) outperformed PET/CT radiomics model (AUC = 0.907, P = 0.0326) or clinical model (AUC = 0.868, P = 0.0036) to predict CD8 expression. In the TCIA cohort, the predicted CD8-high group had significantly higher immune scores and more activated immune pathways than the predicted CD8-low group (P = 0.0421). Conclusion: Our study indicates that 18F-FDG PET/CT radiomics-clinical combined model could be a clinically practical method to non-invasively detect the tumor immune status in NSCLCs.