Reliability and validity of the Chinese version of the kiddie-schedule for affective disorders and schizophrenia-present and lifetime version DSM-5 (K-SADS-PL-C DSM-5).

BACKGROUND: As the Diagnostic and Statistical Manual of Mental Disorders fifth version (DSM-5) was published, the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime version (K-SADS-PL) was modified to adapt the new version (K-SADS-PL DSM-5). We translated it to Chinese (K-SADS-PL-C DSM-5) and described its reliability and validity. METHODS: A total of 154 groups of 6 to 18-year-old children and their guardians were included. Trained interviewers interviewed subjects using the K-SADS-PL-C DSM-5. Interrater reliability was assessed by audio recording. Parent-reported scales, like child behavior checklist (CBCL), the Chinese version of Swan-son Nolan and Pelham, version IV scale-parent form (SNAP-IV), social responsiveness scale (SRS-1), and children-reported scales like depression self-rating scale for children (DSRSC) and the screen for child anxiety related emotional disorders (SCARED) were used to examine the validity of depressive disorder, ADHD, ASD, and ODD. RESULTS: The K-SADS-PL-C DSM-5 had fair to excellent interrater (0.537-1.000) and test-retest (0.468-0.885) reliability of affective disorder and neurodevelopment disorder. The convergent validity of affective disorder and neurodevelopment disorder was good, and their divergent validity was acceptable. LIMITATIONS: i) Clinical questionnaires were insensitive in classifying disorders and had limitations in derived diagnoses. ii) Samples only came from clinical environment, iii) covered limited disease species, and iv) were small. CONCLUSION: The K-SADS-PL-C DSM-5 can support reliable and valid diagnoses for children with affect, neurodevelopmental, and behavioral disorders in China.

Pelvic bones ADC could help to predict severe hematologic toxicity in patients undergoing concurrent chemoradiotherapy for cervical cancer.

BACKGROUND: Hematologic toxicity (HT) during concurrent chemoradiotherapy (CCRT) for cervical cancer can lead to treatment breaks and compromise efficacy. PURPOSE: To evaluate the association between severe hematologic toxicity (HT) and clinical factors and pelvic apparent diffusion coefficient (ADC) during CCRT of cervical cancer patients. METHODS: Data from 120 patients with cervical cancer who were treated with CCRT from January 2016 and December 2018 were retrospectively analyzed. The clinical data (age, menopausal status, clinical stage, body mass index, chemotherapy regimen and chemotherapy cycle) of the patients were collected, and the cohort were divided into two groups based on the HT grade: HT3+ group (HT grade ≥ 3; 66 patients) and HT3- group (HT grade<3; 54 patients). All patients performed MRI before CCRT, and pelvic (ilium, pubis, ischium) ADC value was measured on ADC map. The correlation between severe HT and clinical parameters and pelvic ADC value were analyzed by univariate analysis, and the diagnostic performance was further assessed by receiver operating characteristic (ROC) analysis. RESULTS: In univariate analysis, the menopausal status (p = 0.012) and chemotherapy regimen (p = 0.011) were significantly correlated with severe HT in overall patients, and menopausal patients or patients receiving paclitaxel plus cisplatin (TP) regimen were more likely to develop severe HT. HT3+ group showed a significantly lower pelvic ADC value than HT3- group. The ADC value cut-offs derived from our study for predicting severe HT was 0.317 × 10-3 mm2/s in overall patients. Neither clinical parameters nor pelvic ADCs were associated with severe HT in menopausal patients when analyzed separately (p > 0.05). CONCLUSIONS: Severe HT was significantly associated with menopausal status and chemotherapy regimen in patients with cervical cancer treated with CCRT, and HT3+ group showed a lower pelvic ADC value.

MRI-based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO stage I endometrial carcinoma.

BACKGROUND: Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment. OBJECTIVE: To determine the predictive value of a magnetic resonance imaging (MRI)-based radiomics nomogram for the presence of DMI in the International Federation of Gynecology and Obstetrics (FIGO) stage I EC. METHODS: We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (Center 1) and a validation group (Center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator, and logistic regression analysis with Akaike information criterion to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULT: The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram. CONCLUSION: A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO stage I EC.

MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients.

PURPOSE: This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma. METHODS: A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2-weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance. RESULTS: The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN =0.935, SPE =0.688) in the training group and 0.803 (95% CI: 0.682-0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram. CONCLUSION: Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.

Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma.

BACKGROUND: Preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is potentially important for treatment decisions. PURPOSE: To develop and validate multiparametric magnetic resonance imaging (MRI)-based radiomics methods for preoperative prediction of EGFR mutation based on MRI of SBM. STUDY TYPE: Retrospective. POPULATION: A total of 97 preoperative patients with lumbar SBM from lung adenocarcinoma (77 in training set and 20 in validation set). FIELD STRENGTH/SEQUENCE: T1-weighted, T2-weighted, and T2-weighted fat-suppressed fast spin echo sequences at 3.0 T. ASSESSMENT: Radiomics handcrafted and deep learning-based features were extracted and selected from each MRI sequence. The abilities of the features to predict EGFR mutation status were analyzed and compared. A radiomics nomogram was constructed integrating the selected features. STATISTICAL TESTS: The Mann-Whitney U test and χ2 test were employed for evaluating associations between clinical characteristics and EGFR mutation status for continuous and discrete variables, respectively. Least absolute shrinkage and selection operator was used for selection of predictive features. Sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) were used to evaluate the ability of radiomics models to predict the EGFR mutation. Calibration and decision curve analysis (DCA) were performed to assess and validate nomogram results. RESULTS: The radiomics signature comprised five handcrafted and one deep learning-based features and achieved good performance for predicting EGFR mutation status, with AUCs of 0.891 (95% confidence interval [CI], 0.820-0.962, SEN = 0.913, SPE = 0.710) in the training group and 0.771 (95% CI, 0.551-0.991, SEN = 0.750, SPE = 0.875) in the validation group. DCA confirmed the potential clinical usefulness of the radiomics models. DATA CONCLUSION: Multiparametric MRI-based radiomics is potentially clinical valuable for predicting EGFR mutation status in patients with SBM from lung adenocarcinoma. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: 2.

Establishment and validation of prognostic nomograms to predict overall survival and cancer-specific survival for patients with osteosarcoma.

This study aimed to develop and validate nomograms predicting the survival of osteosarcoma patients from the SEER database and our hospital. Data of 1,066 osteosarcoma patients from the SEER database were randomly divided into a development cohort (n=800) and validation cohort one (n=266). Another cohort of 126 patients from our hospital was utilized as validation cohort two. Univariate and multivariate Cox analyses were performed to identify the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Nomograms predicting the 3- and 5-year OS and CSS probability were constructed and validated. The predictive performances of the established nomograms were evaluated by the concordance index (C-index) and the calibration plot. Variables of age, surgical stage, surgery, grade, tumor site, and tumor size were identified as independent prognosticators for OS and CSS in Cox analyses. The C-indexes for OS and CSS in the development cohort were 0.818 and 0.829. Comparatively, the C-indexes for OS and CSS were 0.843 and 0.834, 0.736 and 0.782 for validation cohort one and two, respectively. Calibration plots showed excellent consistency between nomogram prediction and actual survival. Nomograms based on the SEER database are of high accuracy and can serve as a reliable tool for individualized consultation and survival prediction in osteosarcoma patients.