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Wu L, Li S, Wu C, Wu S, Lin Y, Wei D. Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer. BMC Med Imaging 2024; 24:189. [PMID: 39060962 PMCID: PMC11282842 DOI: 10.1186/s12880-024-01353-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/02/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC). METHODS 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality. RESULTS Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype. CONCLUSION The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.
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Affiliation(s)
- Linyong Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Songhua Li
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Chaojun Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Shaofeng Wu
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Yan Lin
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China
| | - Dayou Wei
- Department of Medical Ultrasound, Maoming People's Hospital, Maoming, 525011, Guangdong, P. R. China.
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AlKhuzaie AA, Jabbar EA, Albadry BJ. Electrolytes, Zinc and Vitamin D 3 in COVID-19 Patients with Cardiovascular Complications. Vopr Virusol 2024; 69:266-276. [PMID: 38996375 DOI: 10.36233/0507-4088-236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Indexed: 07/14/2024]
Abstract
INTRODUCTION COVID-19 is strongly linked to cardiovascular disease, with direct myocardial injury and systemic inflammation as common mechanisms. Pre-existing or infection-induced cardiovascular disease worsens the outcomes for COVID-19 patients. MATERIALS AND METHODS To estimate the serum electrolytes (Na+, K+, Ca++, Zn) and vitamin D3, the study depended on ichroma ii device for Vitamin D3 and Chemistry Analyzer for electrolytes in patient samples. RESULTS A study was conducted on 192 individuals diagnosed with COVID-19, including 35 critical cases, 53 severe cases, 54 moderate cases, and 50 individuals in a control group. The age group with the highest prevalence of infection was between 50‒69 years, while the lowest prevalence was observed in those under 30 years. The study found significant decreases in calcium, potassium, sodium, zinc, and vitamin D3 levels among COVID-19 patients compared to the control group. Zinc and vitamin D3 levels showed a significant correlation with sex, with males experiencing a decline in zinc levels and females having lower vitamin D3 levels. The concentration of calcium, sodium, and zinc showed a negative correlation with age, with older patients having the lowest levels. COVID-19 patients with chronic cardiac issues and high blood pressure exhibited the lowest levels of these markers. The severity of the disease also had a detrimental impact on electrolyte levels, zinc, and vitamin D3, with critical cases showing the lowest levels. The complications such as heart failure were associated with lower levels of potassium, sodium, and zinc. CONCLUSION In conclusion, the study revealed significant associations between COVID-19 and decreased electrolyte levels, zinc, and vitamin D3. Sex and age were found to be correlated with these markers. Patients with chronic cardiac issues and high blood pressure exhibited the lowest levels of these markers. The severity of the disease was also linked to lower electrolyte levels, zinc, and vitamin D3. Complications such as heart failure were associated with decreased levels of potassium, sodium, and zinc.
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Affiliation(s)
- A A AlKhuzaie
- College of Science, University of Thi-Qar
- Ministry of Education, Directorate of Education
| | - E A Jabbar
- Ministry of Education, Directorate of Education
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Yang Y, Xu J, Wang W, Ma M, Huang Q, Zhou C, Zhao J, Duan Y, Luo J, Jiang J, Ye L. A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma. BMC Cancer 2024; 24:438. [PMID: 38594670 PMCID: PMC11005224 DOI: 10.1186/s12885-024-12207-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.
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Affiliation(s)
- Yantao Yang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jing Xu
- Department of Dermatology and Venereal Diseases, Yan'an Hospital of Kunming City, Kunming, China
| | - Wei Wang
- Department of Thoracic and Cardiovascular Surgery, Shiyan Taihe Hospital (Hubei University of Medicine), Hubei, Shiyan, China
| | - Mingsheng Ma
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Qiubo Huang
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Chen Zhou
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jie Zhao
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Yaowu Duan
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China
| | - Jia Luo
- Department of Pathology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiezhi Jiang
- Department of Radiology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lianhua Ye
- Department of Thoracic and Cardiovascular Surgery, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, No. 519 Kunzhou Road, Xishan District, Yunnan Province, Kunming, China.
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Guo W, Li B, Xu W, Cheng C, Qiu C, Sam SK, Zhang J, Teng X, Meng L, Zheng X, Wang Y, Lou Z, Mao R, Lei H, Zhang Y, Zhou T, Li A, Cai J, Ge H. Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy. J Cancer Res Clin Oncol 2024; 150:39. [PMID: 38280037 PMCID: PMC10821966 DOI: 10.1007/s00432-023-05520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/20/2023] [Indexed: 01/29/2024]
Abstract
OBJECTIVE This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). METHODS We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. RESULTS For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). CONCLUSION Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.
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Affiliation(s)
- Wei Guo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Wencai Xu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Chen Cheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Chengyu Qiu
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Sai-Kit Sam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lingguang Meng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Xiaoli Zheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Yuan Wang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Zhaoyang Lou
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Ronghu Mao
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Hongchang Lei
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Ta Zhou
- School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Aijia Li
- Zhengzhou University School of Medicine, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
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Kim S, Yuan L, Kim S, Suh TS. Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO). Radiat Oncol 2024; 19:15. [PMID: 38273278 PMCID: PMC10811833 DOI: 10.1186/s13014-023-02384-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 11/28/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND It is not unusual to see some parts of tissues are excluded in the field of view of CT simulation images. A typical mitigation is to avoid beams entering the missing body parts at the cost of sub-optimal planning. METHODS This study is to solve the problem by developing 3 methods, (1) deep learning (DL) mechanism for missing tissue generation, (2) using patient body outline (PBO) based on surface imaging, and (3) hybrid method combining DL and PBO. The DL model was built upon a Globally and Locally Consistent Image Completion to learn features by Convolutional Neural Networks-based inpainting, based on Generative Adversarial Network. The database used comprised 10,005 CT training slices of 322 lung cancer patients and 166 CT evaluation test slices of 15 patients. CT images were from the publicly available database of the Cancer Imaging Archive. Since existing data were used PBOs were acquired from the CT images. For evaluation, Structural Similarity Index Metric (SSIM), Root Mean Square Error (RMSE) and Peak signal-to-noise ratio (PSNR) were evaluated. For dosimetric validation, dynamic conformal arc plans were made with the ground truth images and images generated by the proposed method. Gamma analysis was conducted at relatively strict criteria of 1%/1 mm (dose difference/distance to agreement) and 2%/2 mm under three dose thresholds of 1%, 10% and 50% of the maximum dose in the plans made on the ground truth image sets. RESULTS The average SSIM in generation part only was 0.06 at epoch 100 but reached 0.86 at epoch 1500. Accordingly, the average SSIM in the whole image also improved from 0.86 to 0.97. At epoch 1500, the average values of RMSE and PSNR in the whole image were 7.4 and 30.9, respectively. Gamma analysis showed excellent agreement with the hybrid method (equal to or higher than 96.6% of the mean of pass rates for all scenarios). CONCLUSIONS It was first demonstrated that missing tissues in simulation imaging could be generated with high similarity, and dosimetric limitation could be overcome. The benefit of this study can be significantly enlarged when MR-only simulation is considered.
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Affiliation(s)
- Sunmi Kim
- Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
- Department of Radiation Oncology, Yonsei Cancer Center, Seoul, 03722, Republic of Korea
| | - Lulin Yuan
- Department of Radiation Oncology, School of Medicine, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Siyong Kim
- Department of Radiation Oncology, School of Medicine, Virginia Commonwealth University, Richmond, VA, 23284, USA.
| | - Tae Suk Suh
- Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
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Melinte OE, Robu Popa D, Dobrin ME, Cernomaz AT, Grigorescu C, Nemes AF, Gradinaru AC, Vicol C, Todea DA, Vulturar DM, Cioroiu IB, Trofor AC. Assessment of Some Risk Factors and Biological Predictors in the Post COVID-19 Syndrome in Asthmatic Patients. J Pers Med 2023; 14:21. [PMID: 38248722 PMCID: PMC10820086 DOI: 10.3390/jpm14010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/15/2023] [Accepted: 12/17/2023] [Indexed: 01/23/2024] Open
Abstract
Long COVID-19 or post-COVID infection (PCI) refers to the prolongation of symptoms in people who have been infected with the SARS-CoV-2 virus. Some meta-analysis studies have shown that patients with comorbidities, such as diabetes, obesity or hypertension, have severe complications after infection with the SARS-CoV-2 virus. The presence of chronic respiratory diseases such as bronchial asthma, COPD, pulmonary hypertension or cystic fibrosis increases the risk of developing severe forms of the COVID-19 disease. The risk of developing the severe form of COVID-19 was observed in patients with bronchial asthma being treated with corticosteroids, but also in those hospitalized with severe asthma. The biological variables determined in patients with PCI infection showed changes, especially in the hematological parameters, but also in some inflammatory markers. The aim of this study was to investigate some biological predictors in post-COVID-19 infection in patients with asthma and various comorbidities. In the case of patients diagnosed with moderate and severe forms of COVID-19, the variation in biological tests has shown high concentrations for serum glucose, lactate dehydrogenase and C-reactive protein. Additionally, the calculation of the relative risk (RR) based on the associated comorbidities in patients with PCI points to higher values for patients with asthma, hypertension, diabetes and obesity (RR moderate/severe form = 0.98/1.52), compared to patients with PCI and asthma (RR moderate/severe form = 0.36/0.63). Based on the statistical results, it can be concluded that the alanine aminotransferase (ALT) activity (p = 0.006) and the age of patients (p = 0.001) are the variables that contribute the most to the separation of the four classes of comorbidities considered.
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Affiliation(s)
- Oana Elena Melinte
- Discipline of Pneumology, III-rd Medical Department, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (O.E.M.); (A.T.C.); (C.V.); (A.C.T.)
| | - Daniela Robu Popa
- Discipline of Pneumology, III-rd Medical Department, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (O.E.M.); (A.T.C.); (C.V.); (A.C.T.)
| | | | - Andrei Tudor Cernomaz
- Discipline of Pneumology, III-rd Medical Department, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (O.E.M.); (A.T.C.); (C.V.); (A.C.T.)
| | - Cristina Grigorescu
- Discipline of the Thoracic Surgery, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | | | - Adina Catinca Gradinaru
- Discipline of Pharmaceutical Botany, Faculty of Pharmacy, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Cristina Vicol
- Discipline of Pneumology, III-rd Medical Department, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (O.E.M.); (A.T.C.); (C.V.); (A.C.T.)
| | - Doina Adina Todea
- Discipline of Pneumology, Department of Medical Sciences, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj, Romania; (D.A.T.); (D.M.V.)
| | - Damiana Maria Vulturar
- Discipline of Pneumology, Department of Medical Sciences, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj, Romania; (D.A.T.); (D.M.V.)
| | - Ionel Bogdan Cioroiu
- Romanian Academy-Iasi Branch, Research Center for Oenology, 700490 Iasi, Romania;
| | - Antigona Carmen Trofor
- Discipline of Pneumology, III-rd Medical Department, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (O.E.M.); (A.T.C.); (C.V.); (A.C.T.)
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Bu S, Pang H, Li X, Zhao M, Wang J, Liu Y, Yu H. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy. BMC Med Imaging 2023; 23:204. [PMID: 38066432 PMCID: PMC10709839 DOI: 10.1186/s12880-023-01169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.
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Affiliation(s)
- Shuting Bu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Huize Pang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaolu Li
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Mengwan Zhao
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Juzhou Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Yu Liu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hongmei Yu
- Department of Neurology, the First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, Liaoning, 110001, PR China.
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Zhu FY, Sun YF, Yin XP, Zhang Y, Xing LH, Ma ZP, Xue LY, Wang JN. Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes. Discov Oncol 2023; 14:224. [PMID: 38055122 DOI: 10.1007/s12672-023-00837-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
OBJECTIVE To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.
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Affiliation(s)
- Feng-Ying Zhu
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Electronic Information Engineering, Hebei University, Baoding, 071002, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Li-Hong Xing
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No.180 of Wusi Road, Lianchi District, Baoding, 071002, China.
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China.
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Zhang M, Yin X, Li W, Zha Y, Zeng X, Zhang X, Cui J, Xue Z, Wang R, Liu C. A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly. BMC Med Imaging 2023; 23:181. [PMID: 37950171 PMCID: PMC10636917 DOI: 10.1186/s12880-023-01145-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 10/29/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in predicting COVID-19 disease exacerbation. METHODS A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed a 3D V-net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted. RESULTS The auto-segmentation framework yielded a dice value 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833 in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was between 0.4 and 0.8 in the validation set or between 0.3 and 0.7 in the test set, it could gain more net benefits using RN than FM and CM. CONCLUSIONS Radiomics features extracted from the adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19.
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Affiliation(s)
- Mudan Zhang
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nan Ming District, 550002, Guiyang, Guiyang, Guizhou Province, China
- School Of Medicine, Guizhou University, 550000, Guiyang, Guizhou province, China
| | - Xuntao Yin
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Wuchao Li
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nan Ming District, 550002, Guiyang, Guiyang, Guizhou Province, China
- School Of Medicine, Guizhou University, 550000, Guiyang, Guizhou province, China
| | - Yan Zha
- Department of Nephrology, Guizhou Provincial People's Hospital, 550002, Guiyang, Guizhou province, China
| | - Xianchun Zeng
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nan Ming District, 550002, Guiyang, Guiyang, Guizhou Province, China
| | - Xiaoyong Zhang
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nan Ming District, 550002, Guiyang, Guiyang, Guizhou Province, China
| | - Jingjing Cui
- Shanghai United Imaging Intelligence, Co., Ltd, 201807, Shanghai, China
| | - Zhong Xue
- Shanghai United Imaging Intelligence, Co., Ltd, 201807, Shanghai, China
| | - Rongpin Wang
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, No. 83 Zhongshan East Road, Nan Ming District, 550002, Guiyang, Guiyang, Guizhou Province, China.
- School Of Medicine, Guizhou University, 550000, Guiyang, Guizhou province, China.
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 400038, Chongqing, China.
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10
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Yao H, Tian L, Liu X, Li S, Chen Y, Cao J, Zhang Z, Chen Z, Feng Z, Xu Q, Zhu J, Wang Y, Guo Y, Chen W, Li C, Li P, Wang H, Luo J. Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study. J Cancer Res Clin Oncol 2023; 149:15827-15838. [PMID: 37672075 PMCID: PMC10620299 DOI: 10.1007/s00432-023-05339-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 08/24/2023] [Indexed: 09/07/2023]
Abstract
PURPOSE There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC). METHODS This two-center retrospective study included 320 patients from the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU) and 132 patients from the Sun Yat-Sen University Cancer Center (SYSUCC). Data from patients at FAHSYSU were divided into a development dataset (n = 267) and a hold-out dataset (n = 53). The development dataset was used to obtain the optimal combination of CT modality and input channel. The hold-out dataset and SYSUCC dataset were used for independent internal and external validation, respectively. RESULTS In the development phase, models trained on unenhanced CT images performed significantly better than those trained on enhanced CT images based on the fivefold cross-validation. The best patient-level performance, with an average area under the receiver operating characteristic curve (AUC) of 0.951 ± 0.026 (mean ± SD), was achieved using the "unenhanced CT and 7-channel" model, which was finally selected as the optimal model. In the independent internal and external validation, AUCs of 0.966 (95% CI 0.919-1.000) and 0.898 (95% CI 0.824-0.972), respectively, were obtained using the optimal model. In addition, the performance of this model was better on large tumors (≥ 40 mm) in both internal and external validation. CONCLUSION The promising results suggest that our multichannel deep learning classifier based on unenhanced whole-tumor CT images is a highly useful tool for differentiating fp-AML from RCC.
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Affiliation(s)
- Haohua Yao
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Urology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Li Tian
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xi Liu
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Shurong Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuhang Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiazheng Cao
- Department of Urology, Jiangmen Central Hospital, Jiangmen, China
| | - Zhiling Zhang
- Department of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zhenhua Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zihao Feng
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Quanhui Xu
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiangquan Zhu
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yinghan Wang
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wei Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Caixia Li
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China
| | - Peixing Li
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Junhang Luo
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
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Lee H, Seo S, Won S, Park WY, Choi JY, Lee KH, Lee SH, Moon SH. Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients. Sci Rep 2023; 13:18247. [PMID: 37880322 PMCID: PMC10600181 DOI: 10.1038/s41598-023-45296-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/18/2023] [Indexed: 10/27/2023] Open
Abstract
In radiomics research, the issue of different instruments being used is significant. In this study, we compared three correction methods to reduce the batch effects in radiogenomic data from fluorodeoxyglucose (FDG) PET/CT images of lung cancer patients. Texture features of the FDG PET/CT images and genomic data were retrospectively obtained. The features were corrected with different methods: phantom correction, ComBat method, and Limma method. Batch effects were estimated using three analytic tools: principal component analysis (PCA), the k-nearest neighbor batch effect test (kBET), and the silhouette score. Finally, the associations of features and gene mutations were compared between each correction method. Although the kBET rejection rate and silhouette score were lower in the phantom-corrected data than in the uncorrected data, a PCA plot showed a similar variance. ComBat and Limma methods provided correction with low batch effects, and there was no significant difference in the results of the two methods. In ComBat- and Limma-corrected data, more texture features exhibited a significant association with the TP53 mutation than in those in the phantom-corrected data. This study suggests that correction with ComBat or Limma methods can be more effective or equally as effective as the phantom method in reducing batch effects.
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Affiliation(s)
- Hyunjong Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Sujin Seo
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Gwanak_1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Sungho Won
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Gwanak_1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Woong-Yang Park
- Department of Molecular Cell Biology, Samsung Medical Center, Samsung Genome Institute, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Kyung-Han Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Se-Hoon Lee
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Li WG, Zeng R, Lu Y, Li WX, Wang TT, Lin H, Peng Y, Gong LG. The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures. BMC Musculoskelet Disord 2023; 24:819. [PMID: 37848859 PMCID: PMC10580519 DOI: 10.1186/s12891-023-06939-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023] Open
Abstract
PURPOSE To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). MATERIALS AND METHODS 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. RESULTS For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. CONCLUSION Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.
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Affiliation(s)
- Wu-Gen Li
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Rou Zeng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Yong Lu
- Department of Radiology, Xinjian County People's Hospital, Nanchang, 330103, China
| | - Wei-Xiang Li
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Tong-Tong Wang
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Huashan Lin
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Changsha, Hunan, 410000, China
| | - Yun Peng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Liang-Geng Gong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China.
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Sheng L, Zhuang L, Yang J, Zhang D, Chen Y, Zhang J, Wang S, Shan G, Du X, Bai X. Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients. BMC Cancer 2023; 23:988. [PMID: 37848844 PMCID: PMC10580570 DOI: 10.1186/s12885-023-11499-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 10/09/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND The machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP. METHODS A total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models. RESULTS The deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009). CONCLUSION Based on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.
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Affiliation(s)
- Liming Sheng
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Lei Zhuang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jing Yang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Danhong Zhang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Ying Chen
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Jie Zhang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Shengye Wang
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Guoping Shan
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Xianghui Du
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China
| | - Xue Bai
- Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
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Li L, Deng H, Ye X, Li Y, Wang J. Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules. Sci Rep 2023; 13:16047. [PMID: 37749121 PMCID: PMC10519965 DOI: 10.1038/s41598-023-42937-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/16/2023] [Indexed: 09/27/2023] Open
Abstract
This study compared the diagnostic efficiency of benign and malignant breast nodules using ultrasonographic characteristics coupled with several machine-learning models, including logistic regression (Logistics), partial least squares discriminant analysis (PLS-DA), linear support vector machine (Linear SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN) and random forest (RF). The clinical information and ultrasonographic characteristics of 926 female patients undergoing breast nodule surgery were collected and their relationships were analyzed using Pearson's correlation. The stepwise regression method was used for variable selection and the Monte Carlo cross-validation method was used to randomly divide these nodule cases into training and prediction sets. Our results showed that six independent variables could be used for building models, including age, background echotexture, shape, calcification, resistance index, and axillary lymph node. In the prediction set, Linear SVM had the highest diagnosis rate of benign nodules (0.881), and Logistics, ANN and LDA had the highest diagnosis rate of malignant nodules (0.910~0.912). The area under the ROC curve (AUC) of Linear SVM was the highest (0.890), followed by ANN (0.883), LDA (0.880), Logistics (0.878), RF (0.874), PLS-DA (0.866), and KNN (0.855), all of which were better than that of individual variances. On the whole, the diagnostic efficacy of Linear SVM was better than other methods.
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Affiliation(s)
- Lu Li
- Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Hongyan Deng
- Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Xinhua Ye
- Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Yong Li
- Institute of Food Safety and Nutrition, Jiangsu Academy of Agricultural Sciences, 50 Zhongling Street, Nanjing, 210014, China.
| | - Jie Wang
- Department of Radiology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China.
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15
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Cheng P, Xie X, Knoedler S, Mi B, Liu G. Predicting overall survival in chordoma patients using machine learning models: a web-app application. J Orthop Surg Res 2023; 18:652. [PMID: 37660044 PMCID: PMC10474690 DOI: 10.1186/s13018-023-04105-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/16/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE The goal of this study was to evaluate the efficacy of machine learning (ML) techniques in predicting survival for chordoma patients in comparison with the standard Cox proportional hazards (CoxPH) model. METHODS Using a Surveillance, Epidemiology, and End Results database of consecutive newly diagnosed chordoma cases between January 2000 and December 2018, we created and validated three ML survival models as well as a traditional CoxPH model in this population-based cohort study. Randomly, the dataset was divided into training and validation datasets. Tuning hyperparameters on the training dataset involved a 1000-iteration random search with fivefold cross-validation. Concordance index (C-index), Brier score, and integrated Brier score were used to evaluate the performance of the model. The receiver operating characteristic (ROC) curves, calibration curves, and area under the ROC curves (AUC) were used to assess the reliability of the models by predicting 5- and 10-year survival probabilities. RESULTS A total of 724 chordoma patients were divided into training (n = 508) and validation (n = 216) cohorts. Cox regression identified nine significant prognostic factors (p < 0.05). ML models showed superior performance over CoxPH model, with DeepSurv having the highest C-index (0.795) and the best discrimination for 5- and 10-year survival (AUC 0.84 and 0.88). Calibration curves revealed strong correlation between DeepSurv predictions and actual survival. Risk stratification by DeepSurv model effectively discriminated high- and low-risk groups (p < 0.01). The optimized DeepSurv model was implemented into a web application for clinical use that can be found at https://hust-chengp-ml-chordoma-app-19rjyr.streamlitapp.com/ . CONCLUSION ML algorithms based on time-to-event results are effective in chordoma prediction, with DeepSurv having the best discrimination performance and calibration.
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Affiliation(s)
- Peng Cheng
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277# Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Xudong Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277# Jiefang Avenue, Wuhan, 430022, Hubei, China
| | - Samuel Knoedler
- Department of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - Bobin Mi
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277# Jiefang Avenue, Wuhan, 430022, Hubei, China.
| | - Guohui Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277# Jiefang Avenue, Wuhan, 430022, Hubei, China.
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Severity of COVID-19 patients with coexistence of asthma and vitamin D deficiency. INFORMATICS IN MEDICINE UNLOCKED 2022; 34:101116. [PMID: 36338941 PMCID: PMC9616486 DOI: 10.1016/j.imu.2022.101116] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/21/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered innumerable health complications, imposing great challenges in managing other respiratory diseases like asthma. Furthermore, increases in the underlying inflammation involved in the fatality of COVID-19 have been linked with lack of vitamin D. In this research work, we intend to investigate the possible genetic linkage of asthma and vitamin D deficiency with the severity and fatality of COVID-19 using a network-based approach. We identified and analysed 41 and 14 differentially expressed genes (DEGs) of COVID-19 being common with asthma and vitamin D deficiency, respectively, through the comparative differential gene expression analysis and their footprints on signalling pathways. Gene set enrichment analysis for GO terms and signalling pathways reveals key biological activities, including inflammatory response-related pathways (e.g., cytokine- and chemokine-mediated signalling pathways, IL-17, and TNF signalling pathways). Besides, the Protein–Protein Interaction network analysis of those DEGs reveals hub proteins, some of which are reported as inflammatory antiviral interferon-stimulated biomarkers that potentially drive the cytokine storm leading to COVID-19 severity and fatality, and contributes in the early stage of viral replication, respectively. Moreover, the regulatory network analysis found these DEGs associated with antiviral and tumour inhibitory transcription factors and micro-RNAs. Finally, drug–target enrichment analysis yields tetradioxin, estradiol, arsenenous acid, and zinc, which have been reported to be effective in suppressing the pro-inflammatory cytokines production, and other respiratory tract infections. Our results yield shared biomarker-driven key hypotheses followed by network-based analytics, demystifying the mechanistic details of COVID-19 comorbidity of asthma and vitamin D deficiency with their potential therapeutic implications.
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