Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making

Mediastinal hemangiopericytoma with neuroforamen invasion

Hemangiopericytoma is a rare tumor and accounts for about 1% of the vascular tumor. The most common site is the lower extremities, retroperitoneum/pelvis fossa, and the head and neck. Mediastinum origin is rare especially with neuroforamen invasion. Herein, we presented a case of 71-year-old woman with primary mediastinal tumor mass with progressive enlargement. She was treated by total T4 laminectomy and partial T3 + T5 laminectomy with intraspinal tumor removal and exploratory thoracotomy with resection of the tumor. She was under postoperative radiotherapy at our Oncology Department. The tumor was under well control until now

Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns

With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management

Synthesis of 3‑(2-Olefinbenzyl)‑4<i>H</i>‑chromen-4-one through Cyclobenzylation and Catalytic C–H Bond Functionalization Using Palladium(II)

An efficient strategy for synthesizing 3-(2-olefinbenzyl)-4<i>H</i>-chromen-4-one in two steps was developed. The first step is a cyclobenzylation reaction between (<i>E</i>)-3-(dimethylamino)-1-(2-hydroxyphenyl)­prop-2-en-1-one and benzyl bromide to produce homoisoflavonoid. The second step involves intermolecular Pd-catalyzed π-chelating-assisted C–H bond olefination. Using the C-2/C-3 double bond of chromone, palladium-catalyzed aryl C–H bond activation can be functionalized to generate <i>ortho</i>-olefination derivatives in moderate to high yields

SRT1720 as an SIRT1 activator for alleviating paraquat-induced models of Parkinson's disease

Epidemiological studies have linked herbicides and Parkinson's disease (PD), with the strongest associations resulting from long exposure durations. Paraquat (PQ), an herbicide, induces PD-like syndromes and has widely been accepted as a PD mimetic. Currently, there is still no cure to prevent the progression of PD, and the search for effective therapeutic ways is urgent. Recently, the impairing activity of sirtuins (SIRTs), such as SIRT1, may correlate with PD etiology. However, the nonspecificity of SIRT1 agonists has made the protective mechanisms against PD unclear and hampered the therapeutic application of SIRT1. Thus, this study investigated the protective mechanism and therapeutic potential of SRT1720, a more specific agonist for SIRT1 synthesized by Sirtris, in alleviating the toxicity of PQ-induced cellular and animal models of PD. Here we show that SRT1720 alleviates PQ-induced toxicity in cell and animal models. Genetic silencing and pharmacological inhibition of SIRT1 attenuated SRT1720's protection against PQ-induced toxicity. Moreover, SRT1720 not only attenuated PQ-induced increased oxidative stress and mitochondrial free radical formations but also decreased mitochondrial membrane potential. Furthermore, SRT1720 reversed PQ-induced decreased PGC-1α levels and mitochondrial biogenesis. Although PQ and SRT1720 elevated NRF2 and antioxidative enzyme levels, only PQ decreased antioxidative enzyme activity but not SRT1720. NRF2 and PGC-1α silencing attenuated SRT1720 protection against PQ-induced toxicity. SRT1720 targeted SIRT1 and activated downstream PGC-1α and NRF2 signalings to prevent PQ-induced toxicity involving oxidative stress and mitochondrial dysfunction. Thus, SRT1720 might have therapeutic potential in preventing PD

Table_3_Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.docx

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.</p