scholarly journals Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261401
Author(s):  
Christian Blüthgen ◽  
Miriam Patella ◽  
André Euler ◽  
Bettina Baessler ◽  
Katharina Martini ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Early Stage ◽  
Tnm Classification ◽  
Tnm Stage ◽  
Imaging Biomarker ◽  
Epithelial Tumor ◽  
Thymic Epithelial Tumor ◽  
Model Training ◽  
Boruta Algorithm

Objectives To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG). Methods Patients with histologically confirmed TET in the years 2000–2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model training, and evaluation (out-of-fold predictions). Shapley additive explanation (SHAP) values were calculated to assess feature importance. Results 105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22–82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3–94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9–93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8–79.5). Conclusions CT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage.

Paraneoplastic myasthenia gravis: Detection of anti-MGT30 (titin) antibodies predicts thymic epithelial tumor

Neurology ◽  
1997 ◽  
Vol 49 (5) ◽  
pp. 1454-1457 ◽  
Author(s):  
R. D. Voltz ◽  
W. C. Albrich ◽  
A. Nägele ◽  
F. Schumm ◽  
M. Wick ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Predictive Value ◽  
Striated Muscle ◽  
Immunofluorescence Assay ◽  
Epithelial Tumor ◽  
Thymic Epithelial Tumors ◽  
Thymic Epithelial Tumor ◽  
Antibody Assays ◽  
Better Than ◽  
Titin Antibodies

It has been suggested that antibodies against nonacetylcholine receptor proteins of striated muscle are markers of the presence of a thymic epithelial tumor in patients with myasthenia gravis (MG). These antibodies may be measured using an immunofluorescence assay against striated muscle(anti-STR) or an ELISA with a recombinant 30-kd titin fragment (anti-MGT30). To directly compare anti-STR with anti-MGT30, we examined the sera of 276 consecutive patients with known or suspected MG. Definite diagnoses and thymic histology, if available, were correlated with the antibody assays. Of the 276 patients, 164 had MG. Thymic histology was obtained in 44 patients: 18 had lymphofollicular hyperplasia, 13 thymic epithelial tumors, 8 atrophy, and 5 were normal. When compared with anti-STR, anti-MGT30 showed a sensitivity of 69% (STR 77%), specificity of 100% (STR 56%, p = 0.026), negative predictive value of 82% (STR 77%), and positive predictive value of 100% (STR 56%, p = 0.003) for the identification of a thymic epithelial tumor versus thymic hyperplasia. We conclude that the anti-MGT30 ELISA is better than the anti-STR immunofluorescence assay for the diagnosis of paraneoplastic MG.

scholarly journals Perplexing Histologic Classification of Thymic Epithelial Tumor

Radiology ◽  
2015 ◽  
Vol 275 (3) ◽  
pp. 929-930 ◽  
Author(s):  
Qijun Shen ◽  
Wenchao Hu ◽  
Zhan Feng
Keyword(s):  
Epithelial Tumor ◽  
Histologic Classification ◽  
Thymic Epithelial Tumor

scholarly journals The IASLC Lung Cancer Staging Project: External Validation of the Revision of the TNM Stage Groupings in the Eighth Edition of the TNM Classification of Lung Cancer

2017 ◽  
Vol 12 (7) ◽  
pp. 1109-1121 ◽  
Author(s):  
Kari Chansky ◽  
Frank C. Detterbeck ◽  
Andrew G. Nicholson ◽  
Valerie W. Rusch ◽  
Eric Vallières ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tnm Classification ◽  
External Validation ◽  
Cancer Staging ◽  
Tnm Stage ◽  
Eighth Edition

Comparing Part-Solid and Pure-Solid Tumors in the TNM Classification of Lung Cancer (Eighth Edition)

2018 ◽  
Vol 67 (04) ◽  
pp. 306-314 ◽  
Author(s):  
Keiji Yamanashi ◽  
Norihito Okumura ◽  
Yoshiharu Yamamoto ◽  
Ayuko Takahashi ◽  
Takashi Nakashima ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Solid Tumors ◽  
Solid Tumor ◽  
Early Stage ◽  
Tnm Classification ◽  
Forced Expiratory Volume ◽  
Component Size ◽  
Early Stage Nsclc ◽  
Eighth Edition

Background In the eighth edition of the TNM classification, the lung tumors that have the same solid components are categorized either as part-solid or pure-solid tumors. However, this is debatable since the tumors in the same T component categories were evaluated without considering this categorization, and was based on a more malignant behavior and a poorer prognosis of pure-solid tumors. The aim of this study was to investigate and compare the prognosis between part-solid and pure-solid tumors. Methods We retrospectively analyzed 530 patients who were diagnosed with clinical-T1a-cN0M0 non–small-cell lung cancer (NSCLC) and were treated surgically. The subjects were divided into part-solid and pure-solid tumor groups using thin-section computed tomography. The prognosis was compared between the groups. Results Although relapse-free survival (RFS) was significantly shorter in the pure-solid tumor group (p = 0.016), no significant differences were observed in the overall survival (OS) between the two groups (p = 0.247). In 137 propensity score–matched pairs, including variables such as age, gender, Brinkman index, body mass index, forced expiratory volume in 1 second/forced vital capacity, Charlson comorbidity index, carcinoembryonic antigen levels, clinical-T status, surgical procedure, and extent of surgery, no significant differences were seen in the RFS and OS between the two groups (p = 0.709 and p = 0.517, respectively). Conclusion In the eighth edition of the TNM classification of clinical-T1a-cN0M0 NSCLC, the prognosis of part-solid and pure-solid tumors showed no significant differences. Solid component size of the tumor is considered important prognostic factor in early-stage NSCLC.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Cross-evaluation of social mining for classification of depressed online personas

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alina Trifan ◽  
José Luis Oliveira
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Post Partum ◽  
Evaluation Study ◽  
Continuous Increase ◽  
Digital Phenotyping ◽  
Public Datasets ◽  
The Impact ◽  
Social Mining

Abstract With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.

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