Based on Parameter Optimization of SVM to Establish an Auxiliary Diagnosis Model of Benign and Malignant Pulmonary Nodules (Preprint)

2020 ◽  
Author(s):  
Jiankun Wang ◽  
Shijie Wang ◽  
Tao Chen ◽  
Yuzhong Hu ◽  
Shuanqiang Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Search Algorithm ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Optimal Parameters ◽  
Solitary Pulmonary Nodules ◽  
Intelligent Algorithm ◽  
Svm Model ◽  
Diagnosis Model ◽  
Grid Search Algorithm

BACKGROUND Solitary pulmonary nodule (SPN) is a common disease in clinic but it is difficult to diagnose[1]. Since most patients have no symptoms when nodules are found, doctors' judgment of nodules is mainly based on their clinical experience, which is highly subjective.Therefore, it is necessary to establish an accurate and objective method for the diagnosis of benign and malignant pulmonary nodules. OBJECTIVE The SVM parameters were optimized by the intelligent algorithm, and the auxiliary diagnosis model of benign and malignant solitary pulmonary nodules combining CT images and serological indicators was constructed, and its test efficiency was evaluated. METHODS CT images and serum indexes of 1030 patients (515 cases of lung cancer and 515 cases of benign pulmonary nodules) diagnosed in our hospital between July 2015 and December 2018 were collected. The CT images of pulmonary nodules were characterized by artificial dimension reduction for feature extraction and assignment,At the same time, the serological indexes were tested; Logistic regression analysis was used to screen CT features and serum indexes of lung cancer; Grid, PSO and GS were used to find the optimal parameters C and g of SVM, and an auxiliary diagnosis model of benign and malignant solitary pulmonary nodules was constructed. RESULTS A total of 9 quantitative image features were extracted from the lung lesion regions segmented from the CT images to describe the phenotypic features of the tumor and their values were successfully assigned. 8 related serological indicators were detected, totaling 17 indicators.The main features of lung cancer including nodule site, edge condition, burr sign, foliation sign, cyfra21-1, scc-ag, CA153 and CA125 were obtained through Logistic regression analysis.Based on the above 8 screening indexes and 17 overall indexes, SVM modeling was carried out after optimization by three intelligent algorithms. The prediction results of the three algorithms in the SVM model with 8 indexes included were as follows: the prediction accuracy of the SVM model under optimization by grid search algorithm was 100%.The accuracy of SVM model was 99.5146% under gga and PSO, and 98.544% under default parameters.The three algorithms were consistent in the prediction results of the SVM model with 17 indexes, and the accuracy reached 100%, while the model accuracy under the default parameters was 88.350%. CONCLUSIONS The accuracy of SVM model can be improved by searching the optimal parameters of SVM with intelligent algorithm.8 relevant indexes screened by the logistic system are selected, and the prediction of the SVM model under optimization by the grid search algorithm can select the least inclusion indexes and guarantee the accuracy, which is the best choice.

scholarly journals Malignant melanoma without primary, presenting as solitary pulmonary nodule: a case report

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
George Tsaknis ◽  
Muhammad Naeem ◽  
Advitya Singh ◽  
Siddharth Vijayakumar
Keyword(s):  
Lung Cancer ◽  
Malignant Melanoma ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Past History ◽  
White Male ◽  
Pulmonary Nodules ◽  
Metastatic Malignant Melanoma ◽  
Unknown Primary ◽  
Solitary Pulmonary Nodules

Abstract Background Solitary pulmonary nodules are the most common incidental finding on chest imaging. Their management is very well defined by several guidelines, with risk calculators for lung cancer being the gold standard. Solitary intramuscular metastasis combined with a solitary pulmonary nodule from malignant melanoma without a primary site is rare. Case presentation A 57-year-old white male was referred to our lung cancer service with solitary pulmonary nodule. After positron-emission tomography, we performed an ultrasound-guided core needle biopsy of an intramuscular solitary lesion, not identified on computed tomography scan, and diagnosed metastatic malignant melanoma. The solitary pulmonary nodule was resected and also confirmed metastatic melanoma. There was no primary skin lesion. The patient received oral targeted therapy and is disease-free 5 years later. Conclusions Clinicians dealing with solitary pulmonary nodules must remain vigilant for other extrathoracic malignancies even in the absence of obvious past history. Lung metastasectomy may have a role in metastatic malignant melanoma with unknown primary.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

scholarly journals A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

2014 ◽  
Vol 33 (1) ◽  
pp. 13 ◽  
Author(s):  
Mehdi Alilou ◽  
Vassili Kovalev ◽  
Eduard Snezhko ◽  
Vahid Taimouri
Keyword(s):  
Early Stage ◽  
Pulmonary Nodules ◽  
Maximum Intensity Projection ◽  
Region Growing ◽  
Ct Images ◽  
Support Vector ◽  
Solitary Pulmonary Nodules ◽  
Computed Tomography Images ◽  
Lung Structure ◽  
Multiple Thresholds

Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

Non-surgical management of early-stage lung cancer

2017 ◽  
Author(s):  
Jim Brown ◽  
Neal Navani
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
The Elderly ◽  
Multidisciplinary Teams ◽  
Solitary Pulmonary Nodules ◽  
Early Stage Lung Cancer ◽  
Mortality And Morbidity ◽  
Inoperable Patient ◽  
Ablative Techniques

As low-dose computed tomography screening of ‘high-risk’ smokers is occurring with increasing frequency, the incidental discovery of solitary pulmonary nodules is becoming more frequent, and lung cancer multidisciplinary teams are now often faced with balancing risk and benefit when making decisions regarding the radical treatment of patients with a clinical diagnosis of early lung cancer but borderline fitness. Surgery offers the best prospect of cure but is associated with significant mortality and morbidity; the elderly and frail experience more toxicity and a greater impact on the quality of life. This chapter reviews the criteria for assessing surgical fitness and examines the evidence for minimally invasive and ablative techniques for the treatment of early peripheral lung cancer in the medically inoperable patient.

scholarly journals Diagnosis and outcome of resected solitary pulmonary nodules after liver transplantation

2019 ◽  
Author(s):  
Mari Tone ◽  
Nobuyasu Awano ◽  
Takehiro Izumo ◽  
Hanako Yoshimura ◽  
Tatsunori Jo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Liver Transplantation ◽  
Early Stage ◽  
Lung Resection ◽  
Invasive Pulmonary Aspergillosis ◽  
Primary Lung Cancer ◽  
Pulmonary Nodules ◽  
Solitary Pulmonary Nodules ◽  
Computed Tomography Scans

Abstract Objective Solitary pulmonary nodules after liver transplantation are challenging clinical problems. Herein, we report the causes and clinical courses of resected solitary pulmonary nodules in patients who underwent liver transplantation. Methods We retrospectively obtained medical records of 68 patients who underwent liver transplantation between March 2009 and June 2016. This study mainly focused on patients with solitary pulmonary nodules observed on computed tomography scans during follow-ups that were conducted until their deaths or February 2019. Results Computed tomography scans revealed solitary pulmonary nodules in 7 of the 68 patients. Definitive diagnoses were obtained using video-assisted lung resection in all seven patients. None experienced major postoperative complications. The final pathologic diagnoses were primary lung cancer in three patients, pulmonary metastases from hepatocellular carcinoma in one patient, invasive pulmonary aspergillosis in one patient, post-transplant lymphoproliferative disorder in one patient, and hemorrhagic infarction in one patient. The three patients with lung cancer were subsequently treated with standard curative resection. Conclusions Solitary pulmonary nodules present in several serious but potentially curable diseases, such as early-stage lung cancer. Patients who present with solitary pulmonary nodules after liver transplantation should be evaluated by standard diagnostic procedures, including surgical biopsy if necessary.

scholarly journals Sputum microRNA Biomarkers for Identifying Lung Cancer in Indeterminate Solitary Pulmonary Nodules

2015 ◽  
Vol 21 (2) ◽  
pp. 484-489 ◽  
Author(s):  
Lingxiao Xing ◽  
Jian Su ◽  
Maria A. Guarnera ◽  
Howard Zhang ◽  
Ling Cai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodules ◽  
Solitary Pulmonary Nodules

Artificial intelligence based on deep learning for differential diagnosis between benign and malignant pulmonary nodules: A real-world, multicenter, diagnostic study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9037-9037
Author(s):  
Tao Xu ◽  
Chuoji Huang ◽  
Yaoqi Liu ◽  
Jing Gao ◽  
Huan Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Test Method ◽  
Histopathological Diagnosis ◽  
Diagnostic Study

9037 Background: Lung cancer is the most common cancer worldwide. Artificial intelligence (AI) platform using deep learning algorithms have made a remarkable progress in improving diagnostic accuracy of lung cancer. But AI diagnostic performance in identifying benign and malignant pulmonary nodules still needs improvement. We aimed to validate a Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) by analyzing computed tomography (CT) imaging data. Methods: This real-world, multicentre, diagnostic study was done in five different tier hospitals in China. The CT images of patients, who were aged over 18 years and never had previous anti-cancer treatments, were retrieved from participating hospitals. 534 eligible patients with 5-30mm diameter pulmonary nodules identified by CT were planning to confirm with histopathological diagnosis. The performance of PNAIDS was also compared with respiratory specialists and radiologists with expert or competent degrees of expertise as well as Mayo Clinic’s model by area under the curve (AUC) and evaluated differences by calculating the 95% CIs using the Z-test method. 11 selected participants were tested circulating genetically abnormal cells (CACs) before surgery with doctors suggested. Results: 611 lung CT images from 534 individuals were used to test PNAIDS. The diagnostic accuracy, valued by AUC, in identifying benign and malignant pulmonary nodules was 0.765 (95%CI [0.729 - 0.798]). The diagnostic sensitivity of PNAIDS is 0.630(0.579 – 0.679), specificity is 0.753 (0.693 – 0.807). PNAIDS achieved diagnostic accuracy similar to that of the expert respiratory specialists (AUC difference: 0.0036 [-0.0426 - 0.0497]; p = 0.8801) and superior when compared with Mayo Clinic’s model (0.120 [0.0649 - 0.176], p < 0·0001), expert radiologists (0.0620 [0.0124 - 0.112], p = 0.0142) and competent radiologists (0.0751 [0.0248 - 0.125], p = 0.0034). 11 selected participants were suggested negative in AI results but positive in respiratory specialists’ result. 8 of them were malignant in histopathological diagnosis with tested more than 3 CACs in their blood. Conclusions: PNAIDS achieved high diagnostic accuracy in differential diagnoses between benign and malignant pulmonary nodules, with diagnostic accuracy similar to that of expert respiratory specialists and was superior to that of Mayo Clinic’s model and radiologists. CACs may be able to assist CT-based AI in improving their effectiveness but it still need more data to be proved. Clinical trial information: ChiCTR1900026233.

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