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Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.

PurposeThis 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.MethodsData 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.ResultsRadiomics 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).ConclusionsRadiomics 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.

Acute myeloid leukemia (AML) is as a highly aggressive and heterogeneous hematological malignancy. MiR-20a-5p has been reported to function as an oncogene or tumor suppressor in several tumors, but the clinical significance and regulatory mechanisms of miR-20a-5p in AML cells have not been fully understood. In this study, we found miR-20a-5p was significantly decreased in bone marrow from AML patients, compared with that in healthy controls. Moreover, decreased miR-20a-5p expression was correlated with risk status and poor survival prognosis in AML patients. Overexpression of miR-20a-5p suppressed cell proliferation, induced cell cycle G0/G1 phase arrest and apoptosis in two AML cell lines (THP-1 and U937) using CCK-8 assay and flow cytometry analysis. Moreover, miR-20a-5p overexpression attenuated tumor growth in vivo by performing tumor xenograft experiments. Luciferase reporter assay and western blot demonstrated that protein phosphatase 6 catalytic subunit ( PPP6C ) as a target gene of miR-20a-5p was negatively regulated by miR-20a-5p in AML cells. Furthermore, PPP6C knockdown imitated, while overexpression reversed the effects of miR-20a-5p overexpression on AML cell proliferation, cell cycle G1/S transition and apoptosis. Taken together, our findings demonstrate that miR-20a-5p / PPP6C represent a new therapeutic target for AML and a potential diagnostic marker for AML therapy.

Background The acquisition of drug resistance has been considered as a main obstacle for cancer chemotherapy. Tumor protein 53 target gene 1 (TP53TG1), a p53-induced lncRNA, plays a vital role in the progression of human cancers. However, little is known about the detailed function and molecular mechanism of TP53TG1 in cisplatin resistance of NSCLC. Methods qRT-PCR analysis was used to detect the expression of TP53TG1, miR-18a and PTEN mRNA in NSCLC tissues and cells. Western blot analysis was performed to determine the protein level of PTEN and cleaved caspase-3. Cell viability and IC50 value were measured by MTT assay. Cell apoptosis was confirmed by flow cytometry assay. Subcellular fractionation assay was used to identify the subcellular location of TP53TG1. Dual-luciferase reporter assay, RNA pull down assay and RNA immunoprecipitation assay were carried out to verify the interaction between TP53TG1 and miR-18a. Xenografts in nude mice were established to verify the effect of TP53TG1 on cisplatin sensitivity of NSCLC cells in vivo. Results TP53TG1 level was downregulated in NSCLC tissues and cell lines. Upregulated TP53TG1 enhanced cisplatin sensitivity and apoptosis of A549/DDP cells, while TP53TG1 depletion inhibited cisplatin sensitivity and apoptosis of A549 cells. TP53TG1 suppressed miR-18a expression in A549 cells. Moreover, TP53TG1-mediated enhancement effect on cisplatin sensitivity was abated following the restoration of miR-18a expression in A549/DDP cells, while si-TP53TG1-induced decrease of cisplatin sensitivity and apoptosis was counteracted by miR-18a inhibitor in A549 cells. Furthermore, TP53TG1 promoted PTEN expression via inhibiting miR-18a. Finally, TP53TG1 sensitized NSCLC cells to cisplatin in vivo. Conclusion TP53TG1 increased the sensitivity of NSCLC cells to cisplatin by modulating miR-18a/PTEN axis, elucidating a novel approach to boost the effectiveness of chemotherapy for NSCLC.

Purpose We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography. Methods Twenty-four thousand eight hundred sixty-six breasts from 12,433 Asian women between August 2012 and December 2018 were enrolled. The study consisted of three parts: (1) evaluation of AIS performance in malignancy diagnosis; (2) stratified analysis of BI-RADS 3–4 subgroups with AIS; and (3) reassessment of BI-RADS 0 breasts with AIS assistance. We further evaluate AIS by conducting a counterbalance-designed AI-assisted study, where ten radiologists read 1302 cases with/without AIS assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score were measured. Results The AIS yielded AUC values of 0.995, 0.933, and 0.947 for malignancy diagnosis in the validation set, testing set 1, and testing set 2, respectively. Within BI-RADS 3–4 subgroups with pathological results, AIS downgraded 83.1% of false-positives into benign groups, and upgraded 54.1% of false-negatives into malignant groups. AIS also successfully assisted radiologists in identifying 7 out of 43 malignancies initially diagnosed with BI-RADS 0, with a specificity of 96.7%. In the counterbalance-designed AI-assisted study, the average AUC across ten readers significantly improved with AIS assistance ( p  = 0.001). Conclusion AIS can accurately detect and diagnose breast cancer on mammography and further serve as a supportive tool for BI-RADS categorization. Critical relevance statement An AI risk assessment tool employing deep learning algorithms was developed and validated for enhancing breast cancer diagnosis from mammograms, to improve risk stratification accuracy, particularly in patients with dense breasts, and serve as a decision support aid for radiologists. Key Points The false positive and negative rates of mammography diagnosis remain high. The AIS can yield a high AUC for malignancy diagnosis. The AIS is important in stratifying BI-RADS categorization. Graphical Abstract

Objectives To develop an [ 18 F]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 [ 18 F]FDG PET/CT and chest [ 18 F]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 [ 18 F]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 18 F-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 [ 18 F]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 [ 18 F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [ 18 F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

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).OBJECTIVESTo 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).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).METHODSA 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).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.RESULTSA 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.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.CONCLUSIONThe [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.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.CLINICAL RELEVANCE STATEMENTA 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.• 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.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.

We present a rare case of cryptococcal lymphadenitis without immunocompromization in a two-and-a-half-year-old child. He was referred to our center with a fifteen-day history of continued fever. Ultrasound and computed tomography (CT) revealed the enlargement of multiple lymph nodes and lung reticulonodular shadows. Hematological, immunological, and microbiological tests for hepatitis, lymphoma, AIDS, and immunoglobulin deficiencies were negative. Laboratory tests demonstrated elevated erythrocyte sedimentation rate, elevated plasma and urinary ß2-microglobulin (ß2-MG) levels, and elevated C-reactive protein and fibrinogen. Both blood routine and bone marrow aspiration showed elevated eosinophil granulocytes. The diagnosis of cryptococcal lymphadenitis was obtained by excisional biopsy of the cervical lymph nodes. The patient was treated with intravenous amphotericin B and oral flucytosine for five weeks, then with subsequent oral fluconazole for three months. The patient is now doing well. Our case suggests that the diagnosis of cryptococcal lymphadenitis is very difficult without etiology and pathology, especially for a patient with a normal immune system; lymph node biopsy is necessary to diagnose it, and immediate antifungal treatment is necessary to treat it.

Core biopsy sampling may not fully capture tumor heterogeneity. Radiomics provides a non-invasive method to assess tumor characteristics, including both the core and surrounding tissue, with the potential to improve the accuracy of HER-2 status prediction. To explore the clinical value of intratumoral and peritumoral radiomics features from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for preoperative prediction of human epidermal growth factor receptor-2 (HER-2) expression status in breast cancer. Two tasks were designed, including Task1-distinguished HER-2 positive and HER-2 negative from 382 breast cancer patients and Task2-distinguished HER-2 low and HER-2 zero expression from 249 patients with HER-2 negative. Three radiomics models (intratumoral, peritumoral 5 mm, intratumoral+peritumoral 5 mm) were constructed based on decision tree, and clinical combined radiomics models were constructed with logistic regression based on clinicopathological features and radscore. The area under the curve (AUC), sensitivity, specificity, accuracy and decision curve analysis (DCA) were used to evaluate the predictive performance of models. Estrogen receptor (ER), progesterone receptor (PR) and Ki67 showed statistically significant in the different groups of HER-2 expression. Additionally, magnetic resonance imaging-reported axillary lymph nodes (MRI-reported ALN) in the positive and negative groups and histological grade in the low and zero expression groups showed significant differences (all < 0.05). For task 1, the peritumoral radiomics model outperformed the other two radiomics models, with AUC values of 0.774 and 0.727 in the training and testing sets, respectively. For task 2, intratumoral + peritumoral radiomics model in the testing set showed the best predictive performance among the three radiomics models, and the AUC values were 0.777. The addition of clinicopathological features slightly altered the AUC values in both tasks. Both radiomics methods based on DCE-MRI and the nomogram are helpful for preoperative prediction of HER-2 expression status.

Histological grade is an acknowledged prognostic factor for breast cancer, essential for determining clinical treatment strategies and prognosis assessment. Our study aims to establish intra- and peritumoral radiomics models using T2WI and DWI MR sequences for predicting the histological grade of breast cancer. 700 breast cancer cases who had MRI scans before surgery were included. The intratumoral region (ITR) of interest was manually delineated, while the peritumoral region (PTR-3 mm) was automatically obtained by expanding the ITR by 3 mm. Radiomics features were extracted using the intra- and peritumoral images from T2WI and DWI sequences on breast MRI. Then, the key features with the strongest predictivity of histological grade were selected. Finally, 9 predictive radiomics models were established based on T2WI-ITR, T2WI-3mmPTR, DWI-ITR, DWI-3mmPTR, T2WI-ITR + 3mmPTR, DWI-ITR + 3mmPTR, (T2WI + DWI)-ITR, (T2WI + DWI)-3mmPTR and (T2WI + DWI)-ITR + 3mmPTR. The (T2WI + DWI)-ITR + 3mmPTR contained 13 DWI features which included a shape feature, a texture feature, and 11 filtered features, as well as 10 T2WI features, all of which were filtered features. Among the 9 models, the combined models showed better performance than the single models in both the training and test sets, especially for the (T2WI + DWI)-ITR + 3mmPTR radiomics model. The (T2WI + DWI)-ITR + 3mmPTR radiomics model achieved a sensitivity, specificity, accuracy, and AUC of 80.4%, 72.4%, 75.0%, and 0.860 in the training set, and 68.9%, 70.5%, 70.0%, and 0.781 in the test set. Decision curve analysis (DCA) showed that the (T2WI + DWI)-ITR + 3mmPTR model had the greatest net clinical benefit compared to the other models. The intra- and peritumoral radiomics methodologies using T2WI and DWI MR sequences could be utilized to assess histological grade for breast cancer, particularly with the (T2WI + DWI)-ITR + 3mmPTR radiomics model demonstrating significant potential for clinical application.