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Liu Y. Editorial: Imaging in non-small cell lung cancer. Front Oncol 2023; 13:1143204. [PMID: 36824140 PMCID: PMC9942925 DOI: 10.3389/fonc.2023.1143204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
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Tan M, Ma W, Yang Y, Duan S, Jin L, Wu Y, Li M. Predictive value of peritumour radiomics in the diagnosis of benign and malignant pulmonary nodules with halo sign. Clin Radiol 2023; 78:e52-e62. [PMID: 36460488 DOI: 10.1016/j.crad.2022.09.130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/05/2022] [Accepted: 09/26/2022] [Indexed: 12/03/2022]
Abstract
AIM To evaluate peritumour radiomics in predicting benign and malignant pulmonary nodules with halo sign. MATERIALS AND METHODS In this retrospective study, 305 pulmonary nodules with halo sign (benign, 120; adenocarcinoma, 185) were collected. Manual segmentation was used to mark the gross tumour volume (GTV) and the peritumour volume (PTV) was established by uniform dilation (1 cm) of the tumour area in three dimensions. The GTV and PTV radiomic features were combined to produce the gross tumour and peritumour volume (GPTV). The minimum-redundancy maximum-relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) algorithm were used to eliminate redundant radiomic features. Predictive models combined with clinical features and radiomic signatures were established. Multivarible logistic regression analysis was used to establish the combined model and develop a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the model. RESULTS In the testing cohort, the area under the ROC curve (AUC) of the GTV, PTV, and GPTV radiomic models was 0.701 (95% CI: 0.589-0.814), 0.674 (95% CI: 0.557-0.791) and 0.755 (95% CI: 0.643-0.867), respectively. The AUC of the nomogram model based on clinical and GPTV radiomic signatures was 0.804 (95% CI: 0.707-0.901). CONCLUSION The nomogram model based on clinical and GPTV radiomic signatures can better predict benign and malignant pulmonary nodules with halo signs, demonstrating that the model has potential as a convenient and effective auxiliary diagnostic tool for radiologists.
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Affiliation(s)
- M Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Chengdu Second People's Hospital, Chengdu, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Shanghai Chest Hospital, Shanghai, China
| | - Y Yang
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Y Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
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Guo Z, Yang J, Zhao L, Yuan J, Yu H. 3D SAACNet with GBM for the classification of benign and malignant lung nodules. Comput Biol Med 2023; 153:106532. [PMID: 36623436 DOI: 10.1016/j.compbiomed.2022.106532] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
In view of the low diagnostic accuracy of the current classification methods of benign and malignant pulmonary nodules, this paper proposes a 3D segmentation attention network integrating asymmetric convolution (SAACNet) classification model combined with a gradient boosting machine (GBM). This can make full use of the spatial information of pulmonary nodules. First, the asymmetric convolution (AC) designed in SAACNet can not only strengthen feature extraction but also improve the network's robustness to object flip and rotation detection and improve network performance. Second, the segmentation attention network integrating AC (SAAC) block can effectively extract more fine-grained multiscale spatial information while adaptively recalibrating multidimensional channel attention weights. The SAACNet also uses a dual-path connection for feature reuse, where the model makes full use of features. In addition, this article makes the loss function pay more attention to difficult and misclassified samples by adding adjustment factors. Third, the GBM is used to splice the nodule size, originally cropped nodule pixels, and the depth features learned by SAACNet to improve the prediction accuracy of the overall model. A comprehensive ablation experiment is carried out on the public dataset LUNA16 and compared with other lung nodule classification models. The classification accuracy (ACC) is 95.18%, and the area under the curve (AUC) is 0.977. The results show that this method effectively improves the classification performance of pulmonary nodules. The proposed method has advantages in the classification of benign and malignant pulmonary nodules, and it can effectively assist radiologists in pulmonary nodule classification.
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Affiliation(s)
- Zhitao Guo
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Jikai Yang
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Linlin Zhao
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Jinli Yuan
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA.
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54
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Giacobbe G, Granata V, Trovato P, Fusco R, Simonetti I, De Muzio F, Cutolo C, Palumbo P, Borgheresi A, Flammia F, Cozzi D, Gabelloni M, Grassi F, Miele V, Barile A, Giovagnoni A, Gandolfo N. Gender Medicine in Clinical Radiology Practice. J Pers Med 2023; 13:jpm13020223. [PMID: 36836457 PMCID: PMC9966684 DOI: 10.3390/jpm13020223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/18/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023] Open
Abstract
Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.
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Affiliation(s)
- Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Piero Trovato
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federica Flammia
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Francesca Grassi
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, 80138 Naples, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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56
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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57
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Peng B, Wang K, Xu R, Guo C, Lu T, Li Y, Wang Y, Wang C, Chang X, Shen Z, Shi J, Xu C, Zhang L. Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer. Front Oncol 2023; 13:1131816. [PMID: 37207163 PMCID: PMC10189057 DOI: 10.3389/fonc.2023.1131816] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/24/2023] [Indexed: 05/21/2023] Open
Abstract
Objectives The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. Methods After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively. Results Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS. Conclusions Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.
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Affiliation(s)
- Bo Peng
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Kaiyu Wang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ran Xu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Congying Guo
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tong Lu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yongchao Li
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yiqiao Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chenghao Wang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoyan Chang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhiping Shen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jiaxin Shi
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chengyu Xu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Linyou Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Linyou Zhang,
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Sethy PK, Geetha Devi A, Padhan B, Behera SK, Sreedhar S, Das K. Lung cancer histopathological image classification using wavelets and AlexNet. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:211-221. [PMID: 36463485 DOI: 10.3233/xst-221301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.
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Affiliation(s)
| | - A Geetha Devi
- Department of Electronics and Communication Engineering, PVP Siddhartha Institute of Technology, Vijayawada, AP, India
| | - Bikash Padhan
- Department of Electronics, Sambalpur University, Jyoti Vihar, Burla, India
| | | | | | - Kalyan Das
- Department Computer Science Engineering and Application, Sambalpur University Institute of Information Technology, Burla, India
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Jiang ZY, Qi LS, Li JT, Cui N, Li W, Liu W, Wang KZ. Radiomics: Status quo and future challenges. Artif Intell Med Imaging 2022; 3:87-96. [DOI: 10.35711/aimi.v3.i4.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.
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Affiliation(s)
- Zhi-Yun Jiang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Li-Shuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Jia-Tong Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Nan Cui
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Wei Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
- Department of Interventional Vascular Surgery, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Wei Liu
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Ke-Zheng Wang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
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Liu W, Zeng C, Wang S, Zhan Y, Huang R, Luo T, Peng G, Wu Y, Qiu Z, Li D, Wu F, Chen C. A combined predicting model for benign esophageal stenosis after simultaneous integrated boost in esophageal squamous cell carcinoma patients (GASTO1072). Front Oncol 2022; 12:1026305. [PMID: 37078004 PMCID: PMC10107369 DOI: 10.3389/fonc.2022.1026305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022] Open
Abstract
PurposeWe aimed to develop a combined predicting model for benign esophageal stenosis (BES) after simultaneous integrated boost (SIB) with concurrent chemotherapy in patients with esophageal squamous cell carcinoma (ESCC).MethodsThis study included 65 patients with EC who underwent SIB with chemotherapy. Esophageal stenosis was evaluated using esophagograms and the severity of eating disorders. Risk factors were investigated using univariate and multivariate analyses. Radiomics features were extracted based on contrast-enhanced CT (CE-CT) before treatment. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature selection and radiomics signature construction. The model’s performance was evaluated using Harrell’s concordance index and receiver operating characteristic curves.ResultsThe patients were stratified into low- and high-risk groups according to BES after SIB. The area under the curves of the clinical model, Rad-score, and the combined model were 0.751, 0.820 and 0.864, respectively. In the validation cohort, the AUCs of these three models were 0.854, 0.883 and 0.917, respectively. The Hosmer-Lemeshow test showed that there was no deviation from model fitting for the training cohort (p=0.451) and validation cohort (p=0.481). The C-indexes of the nomogram were 0.864 and 0.958 for the training and validation cohort, respectively. The model combined with Rad-score and clinical factors achieved favorable prediction ability.ConclusionDefinitive chemoradiotherapy could alleviate tumor-inducing esophageal stenosis but result in benign stenosis. We constructed and tested a combined predicting model for benign esophageal stenosis after SIB. The nomogram incorporating both radiomics signature and clinical prognostic factors showed favorable predictive accuracy for BES in ESCC patients who received SIB with chemotherapy.Trial registration number and date of registrationRegistered in www.Clinicaltrial.gov, ID: NCT01670409, August 12, 2012
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Affiliation(s)
- Weitong Liu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Jieyang People’s Hospital, Jeiyang, China
| | - Chengbing Zeng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Siyan Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yizhou Zhan
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ruihong Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ting Luo
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- Department of Radiation Oncology, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Shanwei, China
| | - Guobo Peng
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yanxuan Wu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Zihan Qiu
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Derui Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Fangcai Wu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Chuangzhen Chen, ; Fangcai Wu,
| | - Chuangzhen Chen
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
- *Correspondence: Chuangzhen Chen, ; Fangcai Wu,
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Dong Z, Chen X, Cheng Z, Luo Y, He M, Chen T, Zhang Z, Qian X, Chen W. Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system. Front Oncol 2022; 12:941744. [PMID: 36591475 PMCID: PMC9802410 DOI: 10.3389/fonc.2022.941744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics features and clinical characteristics for a higher total PCN screening rate. We retrospectively analyzed computed tomography images and clinical data from 129 patients with PCN, including 47 patients with intraductal papillary mucinous neoplasms (IPMNs), 49 patients with serous cystadenomas (SCNs), and 33 patients with mucinous cystic neoplasms (MCNs). Six clinical characteristics and 944 radiomics features were tested, and nine features were finally selected for model construction using DXScore algorithm. A five-fold cross-validation algorithm and a test group were applied to verify the results. In the five-fold cross-validation section, the AUC value of our model was 0.8687, and the total accuracy rate was 74.23%, wherein the accuracy rates of IPMNs, SCNs, and MCNs were 74.26%, 78.37%, and 68.00%, respectively. In the test group, the AUC value was 0.8462 and the total accuracy rate was 73.61%. In conclusion, our research constructed an end-to-end powerful PCN differential diagnosis system based on radiomics method, which could assist decision-making in clinical practice.
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Affiliation(s)
- Zhenglin Dong
- Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,Department of orthopedics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiahan Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhaorui Cheng
- Department of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanbo Luo
- Department of Otorhinolaryngology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min He
- Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Chen
- Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zijie Zhang
- Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Zijie Zhang, ; Xiaohua Qian, ; Wei Chen,
| | - Xiaohua Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Zijie Zhang, ; Xiaohua Qian, ; Wei Chen,
| | - Wei Chen
- Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Zijie Zhang, ; Xiaohua Qian, ; Wei Chen,
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Qiao H, Ding Z, Zhu Y, Wei Y, Xiao B, Zhao Y, Feng Q. Quantitative Analysis of TP53-Related Lung Cancer Based on Radiomics. Int J Gen Med 2022; 15:8481-8489. [PMID: 36510487 PMCID: PMC9739966 DOI: 10.2147/ijgm.s392404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
Background The role of TP53 mutations in the diagnosis and treatment of lung cancer has attracted increasing attention from experts worldwide. This study aimed to explore the expression of TP53 gene in lung cancer and its correlation with radiomics quantitative features. Methods A total of 93 cases of lung cancer confirmed by pathology were selected, including 44 cases with TP53 mutations and 49 cases with TP53 wild-type. ITK-SNAP software was used to segment the pulmonary nodules, AK software was used to extract radiomic features, and a model was established to predict the type of TP53 gene mutation in lung cancer lesions. Results A total of 852 features were extracted, and 10 features remained after feature selection. The accuracy, areas under the curve, specificity, sensitivity, positive predictive value, and negative predictive value of the logistic regression model were 0.80, 0.86, 0.89, 0.74, 0.90, and 0.71, respectively. Conclusion TP53 gene mutations are correlated with radiomic features in lung cancer, which may have application value for TP53 therapy in the future.
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Affiliation(s)
- Hongyu Qiao
- Zhejiang Rongjun Hospital, Jiaxing, People’s Republic of China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
| | - Youcai Zhu
- Zhejiang Rongjun Hospital, Jiaxing, People’s Republic of China
| | - Yuguo Wei
- GE Healthcare Life Sciences, Hangzhou, People’s Republic of China
| | - Baochen Xiao
- Zhejiang Rongjun Hospital, Jiaxing, People’s Republic of China
| | - Yongzhen Zhao
- Zhejiang Rongjun Hospital, Jiaxing, People’s Republic of China
| | - Qi Feng
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China,Correspondence: Qi Feng, Tel +86-13588764520, Email
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Shimada Y, Kudo Y, Maehara S, Amemiya R, Masuno R, Park J, Ikeda N. Radiomics with Artificial Intelligence for the Prediction of Early Recurrence in Patients with Clinical Stage IA Lung Cancer. Ann Surg Oncol 2022; 29:8185-8193. [PMID: 36070112 DOI: 10.1245/s10434-022-12516-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/09/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND We seek to explore the ability of computed tomography (CT)-based radiomics coupled with artificial intelligence (AI) to predict early recurrence (< 2 years after surgery) in patients with clinical stage 0-IA non-small cell lung cancer (c-stage 0-IA NSCLC). PATIENTS AND METHODS Data of 642 patients were collected for early recurrence and assigned to the derivation and validation cohorts at a ratio of 2:1. Using the AI software Beta Version (Fujifilm Corporation, Japan), 39 AI imaging factors, including 17 factors from the AI ground-glass nodule analysis and 22 radiomic features from nodule characterization analysis, were extracted. RESULTS Multivariate analysis showed that male sex (p = 0.016), solid part size (p < 0.001), CT value standard deviation (p = 0.038), solid part volume ratio (p = 0.016), and bronchus translucency (p = 0.007) were associated with recurrence-free survival (RFS). Receiver operating characteristics analysis showed that the area under the curve and optimal cutoff values relevant to recurrence were 0.707 and 1.49 cm for solid part size, and 0.710 and 22.9% for solid part volume ratio, respectively. The 5-year RFS rates for patients in the validation set with solid part size ≤ 1.49 cm and > 1.49 cm were 92.2% and 70.4% (p < 0.001), whereas those for patients with solid part volume ratios ≤ 22.9% and > 22.9% were 97.8% and 71.7% (p < 0.001), respectively. CONCLUSIONS CT-based radiomics coupled with AI contributes to the noninvasive prediction of early recurrence in patients with c-stage 0-IA NSCLC.
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Affiliation(s)
- Yoshihisa Shimada
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan.
| | - Yujin Kudo
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Sachio Maehara
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Ryosuke Amemiya
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Ryuhei Masuno
- Department of Radiology, Tokyo Medical University, Tokyo, Japan
| | - Jinho Park
- Department of Radiology, Tokyo Medical University, Tokyo, Japan
| | - Norihiko Ikeda
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
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Kar SS, Cetin H, Lunasco L, Le TK, Zahid R, Meng X, Srivastava SK, Madabhushi A, Ehlers JP. OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration. OPHTHALMOLOGY SCIENCE 2022; 2:100171. [PMID: 36531588 PMCID: PMC9754979 DOI: 10.1016/j.xops.2022.100171] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/15/2022] [Accepted: 05/12/2022] [Indexed: 06/17/2023]
Abstract
PURPOSE No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. DESIGN Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. PARTICIPANTS Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution). METHODS A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. MAIN OUTCOME MEASURES The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. RESULTS The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. CONCLUSIONS Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.
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Key Words
- 3D, 3-dimensional
- AMD, age-related macular degeneration
- AUC, area under the receiver operating characteristic curve
- AUC-PRC, area under the precision recall curve
- IAI, intravitreal aflibercept injection
- ILM, internal limiting membrane
- IRF, intraretinal fluid
- ML, machine learning
- OCT
- QDA, quadratic discriminant analysis
- RFI, retinal fluid index
- RPE, retinal pigment epithelium
- Radiomics
- SHRM, subretinal hyperreflective material
- SRF, subretinal fluid
- SRFI, subretinal fluid index
- TRFI, total retinal fluid index
- Wet age-related macular degeneration
- mRmR, minimum redundancy maximum relevance
- nAMD, neovascular age-related macular degeneration
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Affiliation(s)
- Sudeshna Sil Kar
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Hasan Cetin
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Leina Lunasco
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Thuy K. Le
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Robert Zahid
- Novartis Pharmaceuticals, East Hanover, New Jersey
| | - Xiangyi Meng
- Novartis Pharmaceuticals, East Hanover, New Jersey
| | - Sunil K. Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
- Vitreoretinal Service, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
| | - Justis P. Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
- Vitreoretinal Service, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio
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Non-Invasive Biomarkers for Early Lung Cancer Detection. Cancers (Basel) 2022; 14:cancers14235782. [PMID: 36497263 PMCID: PMC9739091 DOI: 10.3390/cancers14235782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/27/2022] Open
Abstract
Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.
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Carrera-Escalé L, Benali A, Rathert AC, Martín-Pinardel R, Bernal-Morales C, Alé-Chilet A, Barraso M, Marín-Martinez S, Feu-Basilio S, Rosinés-Fonoll J, Hernandez T, Vilá I, Castro-Dominguez R, Oliva C, Vinagre I, Ortega E, Gimenez M, Vellido A, Romero E, Zarranz-Ventura J. Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis. OPHTHALMOLOGY SCIENCE 2022; 3:100259. [PMID: 36578904 PMCID: PMC9791596 DOI: 10.1016/j.xops.2022.100259] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/25/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
Purpose To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. Design Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965). Participants Patients with type 1 DM and controls included in the progenitor study. Methods Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types. Main Outcome Measures Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types. Results A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets. Conclusions Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Key Words
- AI, artificial intelligence
- AUC, area under the curve
- Artificial intelligence
- DCP, deep capillary plexus
- DM, diabetes mellitus
- DR, diabetic retinopathy
- Diabetic retinopathy
- FR, fundus retinographies
- LDA, linear discriminant analysis
- LR, logistic regression
- ML, machine learning
- Machine learning
- OCT angiography
- OCTA, OCT angiography
- R-DR, referable DR
- RF, random forest
- Radiomics
- SCP, superficial capillary plexus
- SVC, support vector classifier
- rbf, radial basis function
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Affiliation(s)
- Laura Carrera-Escalé
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Anass Benali
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Ann-Christin Rathert
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Ruben Martín-Pinardel
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain,August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | | | - Anibal Alé-Chilet
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Marina Barraso
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Sara Marín-Martinez
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Silvia Feu-Basilio
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Josep Rosinés-Fonoll
- Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Teresa Hernandez
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Irene Vilá
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | | | - Cristian Oliva
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Irene Vinagre
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Spain
| | - Emilio Ortega
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Spain
| | - Marga Gimenez
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Spain
| | - Alfredo Vellido
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Enrique Romero
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center,Department of Computer Science, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Javier Zarranz-Ventura
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain,Institut Clínic d´Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain,Diabetes Unit, Hospital Clínic de Barcelona, Spain,School of Medicine, Universitat de Barcelona, Spain,Correspondence: Javier Zarranz-Ventura, MD, PhD, C/ Sabino Arana 1, Barcelona 08028, Spain.
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Chen H, Lai X, Zhu Y, Huang H, Zeng L, Zhang L. Quantitative proteomics identified circulating biomarkers in lung adenocarcinoma diagnosis. Clin Proteomics 2022; 19:44. [PMID: 36404333 PMCID: PMC9677906 DOI: 10.1186/s12014-022-09381-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 11/06/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Lung cancer (LC) is a common malignant tumor with a high incidence and poor prognosis. Early LC could be cured, but the 5-year-survival rate for patients advanced is extremely low. Early screening of tumor biomarkers through plasma could allow more LC to be detected at an early stage, leading to a earlier treatment and a better prognosis. METHODS This study was based on total proteomic analysis and parallel reaction monitoring validation of peripheral blood from 20 lung adenocarcinoma patients and 20 healthy individuals. Furthermore, differentially expressed proteins closely related to prognosis were analysed using Kaplan-Meier Plotter and receiver operating characteristic curve (ROC) curve analysis. RESULTS The candidate proteins GAPDH and RAC1 showed the highest connectivity with other differentially expressed proteins between the lung adenocarcinoma group and the healthy group using STRING. Kaplan-Meier Plotter analysis showed that lung adenocarcinoma patients with positive ATCR2, FHL1, RAB27B, and RAP1B expression had observably longer overall survival than patients with negative expression (P < 0.05). The high expression of ARPC2, PFKP, PNP, RAC1 was observably negatively correlated with prognosis (P < 0.05). 17 out of 27 proteins showed a high area under the curve (> 0.80) between the lung adenocarcinoma and healthy plasma groups. Among those proteins, UQCRC1 had an area under the curve of 0.960, and 5 proteins had an area under the curve from 0.90 to 0.95, suggesting that these hub proteins might have discriminatory potential in lung adenocarcinoma, P < 0.05. CONCLUSIONS These findings provide UQCRC1, GAPDH, RAC1, PFKP have potential as novel biomarkers for the early screening of lung adenocarcinoma.
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Affiliation(s)
- Hongyu Chen
- grid.13291.380000 0001 0807 1581Key Laboratory of Transplantation Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, Sichuan China
| | - Xiaoqin Lai
- grid.412901.f0000 0004 1770 1022Day Surgery Center, West China Hospital, Sichuan University, Chengdu, Sichuan China
| | - Yihan Zhu
- grid.412901.f0000 0004 1770 1022Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, Sichuan China
| | - Hong Huang
- grid.412901.f0000 0004 1770 1022Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan China
| | - Lingyan Zeng
- grid.412901.f0000 0004 1770 1022Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan China
| | - Li Zhang
- grid.13291.380000 0001 0807 1581Key Laboratory of Transplantation Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Institute of Respiratory Health, West China Hospital, Sichuan University, Chengdu, Sichuan China ,grid.412901.f0000 0004 1770 1022Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan China
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Choi J, Zo S, Kim JH, Oh YJ, Ahn JH, Kim M, Lee K, Lee HY. Nondiagnostic, radial-probe endobronchial ultrasound-guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy. Thorac Cancer 2022; 14:177-185. [PMID: 36408780 PMCID: PMC9834694 DOI: 10.1111/1759-7714.14730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/29/2022] [Accepted: 10/31/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES This study investigated whether radiomic features extracted from radial-probe endobronchial ultrasound (radial EBUS) images can assist in decision-making for subsequent clinical management in cases with indeterminate pathologic results. METHODS A total of 494 patients who underwent radial EBUS biopsy for lung nodules between January 2017 and December 2018 were allocated to our training set. For the validation set, 229 patients with radial EBUS biopsy results from January 2019 to April 2020 were used. A multivariate logistic regression analysis was used for feature selection and prediction modeling. RESULTS In the training set, 157 (67 benign and 90 malignant) of 212 patients pathologically diagnosed as indeterminate were analyzed. In the validation set, 213 patients were diagnosed as indeterminate, and 158 patients (63 benign and 95 malignant) were included in the analysis. The performance of the radiomics-added model, which considered satellite nodules, linear arc, shape, patency of vessels and bronchi, echogenicity, spiculation, C-reactive protein, and minimum histogram, was 0.929 for the training set and 0.877 for the validation set, whereas the performance of the model without radiomics was 0.910 and 0.891, respectively. CONCLUSION Although the next diagnostic step for indeterminate lung biopsy results remains controversial, integrating various factors, including radiomic features from radial EBUS, might facilitate decision-making for subsequent clinical management.
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Affiliation(s)
- Jihwan Choi
- Department of Digital HealthSAIHST, Sungkyunkwan UniversitySeoulSouth Korea,Hongseong‐gun Public Health CenterHongseong‐gunSouth Korea
| | - Sungmin Zo
- Division of Pulmonary and Critical Care Medicine, Department of MedicineSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
| | - Jong Hoon Kim
- Industrial Biomaterial Research Center, Korea Research Institute of Bioscience and BiotechnologyDaejeonSouth Korea
| | - You Jin Oh
- Department of Health Sciences and TechnologySAIHST, Sungkyunkwan UniversitySeoulSouth Korea
| | - Joong Hyun Ahn
- Biomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center
| | - Myoungkyoung Kim
- Department of Radiology and Center for Imaging ScienceSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Department of MedicineSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
| | - Ho Yun Lee
- Department of Health Sciences and TechnologySAIHST, Sungkyunkwan UniversitySeoulSouth Korea,Department of Radiology and Center for Imaging ScienceSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulSouth Korea
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A Variable-Clustering-Based Feature Selection to Improve Positive and Negative Discrimination of P53 Protein in Colorectal Cancer Patients. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9261713. [DOI: 10.1155/2022/9261713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022]
Abstract
P53 protein tumor suppressor gene plays a guiding role in the treatment and prognosis of colorectal cancer (CRC). This paper aimed at proposing a feature selection method based on variable clustering to improve positive and negative discrimination of P53 protein in CRC patients. In this approach, we cluster the preprocessed dataset with variables, and then find the features with the largest information value (IV) for each cluster to form a feature subset. We call this method as IV_Cluster. In the actual medical data test, compared with the information value feature selection method, the accuracy of the 10-fold cross-validation logistic regression model increased by 4.4%, 2.0%, and 5.8%, and Kappa values increased by 21.8%, 8.6%, and 22.4%, respectively, under 5, 10, and 15 feature sets.
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Chen NB, Xiong M, Zhou R, Zhou Y, Qiu B, Luo YF, Zhou S, Chu C, Li QW, Wang B, Jiang HH, Guo JY, Peng KQ, Xie CM, Liu H. CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment. Radiat Oncol 2022; 17:184. [PMID: 36384755 PMCID: PMC9667605 DOI: 10.1186/s13014-022-02136-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these patients. We aimed to identify pretreatment computed tomography-based radiomics features extracted from tumor and tumor organismal environment (TOE) for long-term survival prediction in these patients treated with CCRT. Methods A total of 298 eligible patients were randomly assigned into the training cohort and validation cohort with a ratio 2:1. An integrated feature selection and model training approach using support vector machine combined with genetic algorithm was performed to predict 3-year overall survival (OS). Patients were stratified into the high-risk and low-risk group based on the predicted survival status. Pulmonary function test and blood gas analysis indicators were associated with radiomic features. Dynamic changes of peripheral blood lymphocytes counts before and after CCRT had been documented. Results Nine features including 5 tumor-related features and 4 pulmonary features were selected in the predictive model. The areas under the receiver operating characteristic curve for the training and validation cohort were 0.965 and 0.869, and were reduced by 0.179 and 0.223 when all pulmonary features were excluded. Based on radiomics-derived stratification, the low-risk group yielded better 3-year OS (68.4% vs. 3.3%, p < 0.001) than the high-risk group. Patients in the low-risk group had better baseline FEV1/FVC% (96.3% vs. 85.9%, p = 0.046), less Grade ≥ 3 lymphopenia during CCRT (63.2% vs. 83.3%, p = 0.031), better recovery of lymphopenia from CCRT (71.4% vs. 27.8%, p < 0.001), lower incidence of Grade ≥ 2 radiation-induced pneumonitis (31.6% vs. 53.3%, p = 0.040), superior tumor remission (84.2% vs. 66.7%, p = 0.003). Conclusion Pretreatment radiomics features from tumor and TOE could boost the long-term survival forecast accuracy in LANSCLC patients, and the predictive results could be utilized as an effective indicator for survival risk stratification. Low-risk patients might benefit more from radical CCRT and further adjuvant immunotherapy. Trial registration: retrospectively registered. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-022-02136-w.
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Sun Y, Zhao W, Kuang K, Jin L, Gao P, Duan S, Xiao Y, Liu J, Li M. Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma. Front Med (Lausanne) 2022; 9:939434. [DOI: 10.3389/fmed.2022.939434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
ObjectiveThis study aimed to assess the value of radiomics based on non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) images in the preoperative discrimination between lung invasive adenocarcinomas (IAC) and non-invasive adenocarcinomas (non-IAC).MethodsWe enrolled 1,185 pulmonary nodules (478 non-IACs and 707 IACs) to build and validate radiomics models. An external testing set comprising 63 pulmonary nodules was collected to verify the generalization of the models. Radiomic features were extracted from both NCCT and CECT images. The predictive performance of radiomics models in the validation and external testing sets were evaluated and compared with radiologists’ evaluations. The predictive performances of the radiomics models were also compared between three subgroups in the validation set (Group 1: solid nodules, Group 2: part-solid nodules, and Group 3: pure ground-glass nodules).ResultsThe NCCT, CECT, and combined models showed good ability to discriminate between IAC and non-IAC [respective areas under the curve (AUCs): validation set = 0.91, 0.90, and 0.91; Group 1 = 0.82, 0.79, and 0.81; Group 2 = 0.93, 0.92, and 0.93; and Group 3 = 0.90, 0.90, and 0.89]. In the external testing set, the AUC of the three models were 0.89, 0.91, and 0.89, respectively. The accuracies of these three models were comparable to those of the senior radiologist and better those that of the junior radiologist.ConclusionRadiomic models based on CT images showed good predictive performance in discriminating between lung IAC and non-IAC, especially in part solid nodule group. However, radiomics based on CECT images provided no additional value compared to NCCT images.
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MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy. Sci Rep 2022; 12:18631. [PMID: 36329116 PMCID: PMC9633752 DOI: 10.1038/s41598-022-22826-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.
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Teng X, Zhang J, Ma Z, Zhang Y, Lam S, Li W, Xiao H, Li T, Li B, Zhou T, Ren G, Lee FKH, Au KH, Lee VHF, Chang ATY, Cai J. Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma. Front Oncol 2022; 12:974467. [PMID: 36313629 PMCID: PMC9614273 DOI: 10.3389/fonc.2022.974467] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundUsing high robust radiomic features in modeling is recommended, yet its impact on radiomic model is unclear. This study evaluated the radiomic model’s robustness and generalizability after screening out low-robust features before radiomic modeling. The results were validated with four datasets and two clinically relevant tasks.Materials and methodsA total of 1,419 head-and-neck cancer patients’ computed tomography images, gross tumor volume segmentation, and clinically relevant outcomes (distant metastasis and local-regional recurrence) were collected from four publicly available datasets. The perturbation method was implemented to simulate images, and the radiomic feature robustness was quantified using intra-class correlation of coefficient (ICC). Three radiomic models were built using all features (ICC > 0), good-robust features (ICC > 0.75), and excellent-robust features (ICC > 0.95), respectively. A filter-based feature selection and Ridge classification method were used to construct the radiomic models. Model performance was assessed with both robustness and generalizability. The robustness of the model was evaluated by the ICC, and the generalizability of the model was quantified by the train-test difference of Area Under the Receiver Operating Characteristic Curve (AUC).ResultsThe average model robustness ICC improved significantly from 0.65 to 0.78 (P< 0.0001) using good-robust features and to 0.91 (P< 0.0001) using excellent-robust features. Model generalizability also showed a substantial increase, as a closer gap between training and testing AUC was observed where the mean train-test AUC difference was reduced from 0.21 to 0.18 (P< 0.001) in good-robust features and to 0.12 (P< 0.0001) in excellent-robust features. Furthermore, good-robust features yielded the best average AUC in the unseen datasets of 0.58 (P< 0.001) over four datasets and clinical outcomes.ConclusionsIncluding robust only features in radiomic modeling significantly improves model robustness and generalizability in unseen datasets. Yet, the robustness of radiomic model has to be verified despite building with robust radiomic features, and tightly restricted feature robustness may prevent the optimal model performance in the unseen dataset as it may lower the discrimination power of the model.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Francis Kar-ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Kwok-hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Amy Tien Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium and Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Jing Cai,
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Lu S, Ling H, Chen J, Tan L, Gao Y, Li H, Tan P, Huang D, Zhang X, Liu Y, Mao Y, Qiu Y. MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma. Front Oncol 2022; 12:936040. [PMID: 36212477 PMCID: PMC9539826 DOI: 10.3389/fonc.2022.936040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/06/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo investigate the role of pre-treatment magnetic resonance imaging (MRI) radiomics for the preoperative prediction of lymph node (LN) metastasis in patients with hypopharyngeal squamous cell carcinoma (HPSCC).MethodsA total of 155 patients with HPSCC were eligibly enrolled from single institution. Radiomics features were extracted from contrast-enhanced axial T-1 weighted (CE-T1WI) sequence. The most relevant features of LN metastasis were selected by the least absolute shrinkage and selection operator (LASSO) method. Univariate and multivariate logistic regression analysis was adopted to determine the independent clinical risk factors. Three models were constructed to predict the LN metastasis status: one using radiomics only, one using clinical factors only, and the other one combined radiomics and clinical factors. Receiver operating characteristic (ROC) curves and calibration curve were used to evaluate the discrimination and the accuracy of the models, respectively. The performances were tested by an internal validation cohort (n=47). The clinical utility of the models was assessed by decision curve analysis.ResultsThe nomogram consisted of radiomics scores and the MRI-reported LN status showed satisfactory discrimination in the training and validation cohorts with AUCs of 0.906 (95% CI, 0.840 to 0.972) and 0.853 (95% CI, 0.739 to 0.966), respectively. The nomogram, i.e., the combined model, outperformed the radiomics and MRI-reported LN status in both discrimination and clinical usefulness.ConclusionsThe MRI-based radiomics nomogram holds promise for individual and non-invasive prediction of LN metastasis in patients with HPSCC.
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Affiliation(s)
- Shanhong Lu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hang Ling
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
| | - Juan Chen
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Lei Tan
- College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, China
| | - Yan Gao
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Huayu Li
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Pingqing Tan
- Department of Head and Neck Surgery, Hunan Cancer Hospital, Xiangya Medical School, Central South University, Changsha, China
| | - Donghai Huang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Zhang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Liu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanzheng Qiu, ; Yitao Mao,
| | - Yuanzheng Qiu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanzheng Qiu, ; Yitao Mao,
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Gorodetski B, Becker PH, Baur ADJ, Hartenstein A, Rogasch JMM, Furth C, Amthauer H, Hamm B, Makowski M, Penzkofer T. Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning. Eur Radiol Exp 2022; 6:44. [PMID: 36104467 PMCID: PMC9474782 DOI: 10.1186/s41747-022-00296-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 07/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. Methods This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists. Results All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75−0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit. Conclusions Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00296-8.
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Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features. Radiol Med 2022; 127:1085-1097. [DOI: 10.1007/s11547-022-01549-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/18/2022] [Indexed: 10/14/2022]
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Zhang T, Zhang C, Zhong Y, Sun Y, Wang H, Li H, Yang G, Zhu Q, Yuan M. A radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm. Front Oncol 2022; 12:900049. [PMID: 36033463 PMCID: PMC9406823 DOI: 10.3389/fonc.2022.900049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/20/2022] [Indexed: 12/04/2022] Open
Abstract
Objective To investigate whether radiomics can help radiologists and thoracic surgeons accurately predict invasive adenocarcinoma (IAC) manifesting as part-solid nodules (PSNs) with solid components <6 mm and provide a basis for rational clinical decision-making. Materials and Methods In total, 1,210 patients (mean age ± standard deviation: 54.28 ± 11.38 years, 374 men and 836 women) from our hospital and another hospital with 1,248 PSNs pathologically diagnosed with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or IAC were enrolled in this study. Among them, 1,050 cases from our hospital were randomly divided into a derivation set (n = 735) and an internal validation set (n = 315), 198 cases from another hospital were used for external validation. Each labeled nodule was segmented, and 105 radiomics features were extracted. Least absolute shrinkage and selection operator (LASSO) was used to calculate Rad-score and build the radiomics model. Multivariable logistic regression was conducted to identify the clinicoradiological predictors and establish the clinical-radiographic model. The combined model and predictive nomogram were developed based on identified clinicoradiological independent predictors and Rad-score using multivariable logistic regression analysis. The predictive performances of the three models were compared via receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was performed on both the internal and external validation sets to evaluate the clinical utility of the nomogram. Results The radiomics model showed superior predictive performance than the clinical-radiographic model in both internal and external validation sets (Az values, 0.884 vs. 0.810, p = 0.001; 0.924 vs. 0.855, p < 0.001, respectively). The combined model showed comparable predictive performance to the radiomics model (Az values, 0.887 vs. 0.884, p = 0.398; 0.917 vs. 0.924, p = 0.271, respectively). The clinical application value of the nomogram developed based on the Rad-score, maximum diameter, and lesion shape was confirmed, and DCA demonstrated that application of the Rad-score would be beneficial for radiologists predicting invasive lesions. Conclusions Radiomics has the potential as an independent diagnostic tool to predict the invasiveness of PSNs with solid components <6 mm.
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Affiliation(s)
- Teng Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yan Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Haijie Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Quan Zhu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Quan Zhu, ; Mei Yuan,
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Quan Zhu, ; Mei Yuan,
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Tan P, Huang W, Wang L, Deng G, Yuan Y, Qiu S, Ni D, Du S, Cheng J. Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images. Front Physiol 2022; 13:978222. [PMID: 35957985 PMCID: PMC9358138 DOI: 10.3389/fphys.2022.978222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.
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Affiliation(s)
- Peixin Tan
- Department of Radiation Oncology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wei Huang
- Department of Radiation Oncology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lingling Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Guanhua Deng
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, China
| | - Ye Yuan
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Shili Qiu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Shasha Du
- Department of Radiation Oncology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Shasha Du, ; Jun Cheng,
| | - Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
- *Correspondence: Shasha Du, ; Jun Cheng,
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Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
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Bianconi F, Palumbo I, Fravolini ML, Rondini M, Minestrini M, Pascoletti G, Nuvoli S, Spanu A, Scialpi M, Aristei C, Palumbo B. Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans. SENSORS (BASEL, SWITZERLAND) 2022; 22:5044. [PMID: 35808538 PMCID: PMC9269784 DOI: 10.3390/s22135044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/28/2022] [Accepted: 07/02/2022] [Indexed: 06/15/2023]
Abstract
Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann-Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions.
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Affiliation(s)
- Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy;
| | - Isabella Palumbo
- Section of Radiation Oncology, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (I.P.); (C.A.)
| | - Mario Luca Fravolini
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy;
| | - Maria Rondini
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (M.R.); (S.N.); (A.S.)
| | - Matteo Minestrini
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (M.M.); (B.P.)
| | - Giulia Pascoletti
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy;
| | - Susanna Nuvoli
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (M.R.); (S.N.); (A.S.)
| | - Angela Spanu
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy; (M.R.); (S.N.); (A.S.)
| | - Michele Scialpi
- Division of Diagnostic Imaging, Department of Medicine and Surgery, Piazza Lucio Severi 1, 06132 Perugia, Italy;
| | - Cynthia Aristei
- Section of Radiation Oncology, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (I.P.); (C.A.)
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (M.M.); (B.P.)
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Ding H, Li J, Jiang K, Gao C, Lu L, Zhang H, Chen H, Gao X, Zhou K, Sun Z. Assessing the inflammatory severity of the terminal ileum in Crohn disease using radiomics based on MRI. BMC Med Imaging 2022; 22:118. [PMID: 35787255 PMCID: PMC9254684 DOI: 10.1186/s12880-022-00844-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 06/21/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Evaluating inflammatory severity using imaging is essential for Crohn's disease, but it is limited by potential interobserver variation and subjectivity. We compared the efficiency of magnetic resonance index of activity (MaRIA) collected by radiologists and a radiomics model in assessing the inflammatory severity of terminal ileum (TI). METHODS 121 patients were collected from two centers. Patients were divided into ulcerative group and mucosal remission group based on the TI Crohn's disease Endoscopic Severity Index. The consistency of bowel wall thickness (BWT), relative contrast enhancement (RCE), edema, ulcer, MaRIA and features of the region of interest between radiologists were described by weighted Kappa test and intraclass correlation coefficient (ICC), and developed receiver operating curve of MaRIA. The radiomics model was established using reproducible features of logistic regression based on arterial staging of T1WI sequences. Delong test was used to compare radiomics with MaRIA. RESULTS The consistency between radiologists were moderate in BWT (ICC = 0.638), fair in edema (κ = 0.541), RCE (ICC = 0.461), MaRIA (ICC = 0.579) and poor in ulcer (κ = 0.271). Radiomics model was developed by 6 reproducible features (ICC = 0.93-0.96) and equivalent to MaRIA which evaluated by the senior radiologist (0.872 vs 0.883 in training group, 0.824 vs 0.783 in validation group, P = 0.847, 0.471), both of which were significantly higher than MaRIA evaluated by junior radiologist (AUC: 0.621 in training group, 0.557 in validation group, all, P < 0.05). CONCLUSION The evaluation of inflammatory severity could be performed by radiomics objectively and reproducibly, and was comparable to MaRIA evaluated by the senior radiologist. Radiomics may be an important method to assist junior radiologists to assess the severity of inflammation objectively and accurately.
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Affiliation(s)
- Honglei Ding
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Jiaying Li
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Kefang Jiang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Liangji Lu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Huani Zhang
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Haibo Chen
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Xuning Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China
| | - Kefeng Zhou
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, People's Republic of China.
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Kubo K, Kawahara D, Murakami Y, Takeuchi Y, Katsuta T, Imano N, Nishibuchi I, Saito A, Konishi M, Kakimoto N, Yoshioka Y, Toratani S, Ono S, Ueda T, Takeno S, Nagata Y. Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:93-101. [PMID: 35431177 DOI: 10.1016/j.oooo.2021.12.122] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/10/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography. STUDY DESIGN This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE). RESULTS For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98. CONCLUSIONS Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.
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Affiliation(s)
- Katsumaro Kubo
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
| | - Yuki Takeuchi
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsuyoshi Katsuta
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuki Imano
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ikuno Nishibuchi
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Akito Saito
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Masaru Konishi
- Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yukio Yoshioka
- Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shigeaki Toratani
- Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shigehiro Ono
- Department of Oral and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tsutomu Ueda
- Department of Otolaryngology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Department of Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Sachio Takeno
- Department of Otolaryngology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Department of Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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84
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Benign-malignant classification of pulmonary nodule with deep feature optimization framework. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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85
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Tomassini S, Falcionelli N, Sernani P, Burattini L, Dragoni AF. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey. Comput Biol Med 2022; 146:105691. [PMID: 35691714 DOI: 10.1016/j.compbiomed.2022.105691] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
Abstract
Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Nicola Falcionelli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Paolo Sernani
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Aldo Franco Dragoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
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Tao J, Liang C, Yin K, Fang J, Chen B, Wang Z, Lan X, Zhang J. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer. Diagn Interv Imaging 2022; 103:535-544. [PMID: 35773100 DOI: 10.1016/j.diii.2022.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning. MATERIALS AND METHODS A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS. RESULTS For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.
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Affiliation(s)
- Junli Tao
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Changyu Liang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Ke Yin
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Jiayang Fang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Bohui Chen
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Zhenyu Wang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400030 PR China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing 400044, PR China.
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Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis? Eur Radiol 2022; 32:8540-8549. [PMID: 35731290 DOI: 10.1007/s00330-022-08882-5] [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: 04/28/2022] [Revised: 04/28/2022] [Accepted: 05/12/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To explore the utility of radiomics and deep learning model in assessing the risk factors for sepsis after flexible ureteroscopy lithotripsy (FURL) or percutaneous nephrolithotomy (PCNL) in patients with ureteral calculi. METHODS This retrospective analysis included 847 patients with treatment-naive proximal ureteral calculi who received FURL or PCNL. All participants were preoperatively conducted non-contrast computed tomography scans, and relevant clinical information was meanwhile collected. After propensity score matching, the radiomics model was established to predict the onset of sepsis. A deep learning model was also adapted to further improve the prediction accuracy. Performance of these trained models was verified in another independent external validation set including 40 cases of ureteral calculi patients. RESULTS The overall incidence of sepsis after FURL or PCNL was 5.9%. The least absolute shrinkage and selection operator (LASSO) regression analysis revealed 26 predictive variables, with an overall AUC of 0.881 (95% CI, 0.813-0.931) and an AUC of 0.783 (95% CI, 0.766-0.801) in external validation cohort. Judicious adaption of a deep neural network (DNN) model to our dataset improved the AUC to 0.920 (95% CI, 0.906-0.933) in the internal validation. To eliminate the overfitting, external validation was carried out for DNN model (AUC = 0.874 (95% CI, 0.858-0.891)). CONCLUSIONS The DNN was more effective than the LASSO model in revealing risk factors for sepsis after FURL or PCNL in single ureteral calculi patients, and females are more susceptible to sepsis than males. Deep learning models have the potential to act as gatekeepers to facilitate patient stratification. KEY POINTS • Both the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models were shown to be effective in sepsis prediction. • The DNN model achieved superior prediction capability, with an AUC of 0.920 (95% CI, 0.906-0.933). • DNN-assisted model has potential to serve as a gatekeeper to facilitate patient stratification.
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Xie F, Zheng K, Liu L, Jin X, Fu L, Zhu Z. A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions. Front Oncol 2022; 12:877501. [PMID: 35720018 PMCID: PMC9201288 DOI: 10.3389/fonc.2022.877501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background This is a pilot study of radiomics based on 68Ga-NOTA-PRGD2 [NOTA-PEG4-E[c(RGDfK)]2)] and 18F-FDG PET/CT to (i) evaluate the diagnostic efficacy of radiomics features of 68Ga-NOTA-PRGD2 PET in the differential diagnosis of benign and malignant pulmonary space-occupying lesions and (ii) compare the diagnostic efficacy of multi-modality and multi-probe images. Methods We utilized a dataset of 48 patients who participated in 68Ga-NOTA-PRGD2 PET/CT and 18F-FDG PET/CT clinical trials to extract image features and evaluate their diagnostic efficacy in the differentiation of benign and malignant lesions by the Mann-Whitney U test. After feature selection with sequential forward selection, random forest models were developed with tenfold cross-validation. The diagnostic performance of models based on different image features was visualized by receiver operating characteristic (ROC) curves and compared by permutation tests. Results Fourteen of the 68Ga-NOTA-PRGD2 PET features between benign and malignant pulmonary space-occupying lesions had significant differences (P<0.05, Mann-Whitney U test). Eighteen of the 68Ga-NOTA-PRGD2 PET features demonstrated higher AUC values than all CT features in the differential diagnosis of pulmonary lesions. The AUC value (0.908) of the three-modal feature model was significantly higher (P<0.05, permutation test) than those of the single- and dual-modal models. Conclusion 68Ga-NOTA-PRGD2 PET features have better diagnostic capacity than CT features for pulmonary space-occupying lesions. The combination of multi-modality and multi-probe images can improve the diagnostic efficiency of models. Our preliminary clinical hypothesis of using radiomics based on 68Ga-NOTA-PRGD2 PET images and multimodal images as a diagnostic tool warrants further validation in a larger multicenter sample size.
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Affiliation(s)
- Fei Xie
- Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Department of Nuclear Medicine, Peking Union Medical College Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Science and Peking Union Medical College (PUMC), Beijing, China.,Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Kun Zheng
- Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Department of Nuclear Medicine, Peking Union Medical College Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Science and Peking Union Medical College (PUMC), Beijing, China
| | - Linwen Liu
- Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Department of Nuclear Medicine, Peking Union Medical College Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Science and Peking Union Medical College (PUMC), Beijing, China
| | - Xiaona Jin
- Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Department of Nuclear Medicine, Peking Union Medical College Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Science and Peking Union Medical College (PUMC), Beijing, China
| | - Lilan Fu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhaohui Zhu
- Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Department of Nuclear Medicine, Peking Union Medical College Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Science and Peking Union Medical College (PUMC), Beijing, China
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89
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Yang Y, Tan M, Ma W, Duan S, Huang X, Jin L, Tang L, Li M. Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram. Clin Radiol 2022; 77:e680-e688. [PMID: 35718542 DOI: 10.1016/j.crad.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/05/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
AIM To develop and validate a radiomics nomogram for prediction of degree of differentiation in lung adenocarcinoma presenting as sub-solid or solid nodules. MATERIALS AND METHODS A total of 438 patients with histopathologically confirmed adenocarcinoma (248 non-poorly differentiated and 190 poorly differentiated) were divided into training cohort (n=235) and internal validation cohort (n=203) according to surgery sequence. Sixty patients form public TCIA dataset were selected for external validation. One thousand, two hundred and eighteen radiomics features were extracted from each volumetric region of interest and a least absolute shrinkage and selection operator logistic regression was applied to select meaningful radiomic features for building a radiomics score (Rad-score) model. A nomogram model incorporating the Rad-score and type was established after multivariable logistic regression. The discrimination efficiency, calibration efficacy, and clinical utility value of the nomogram were evaluated. RESULTS The Rad-score model could predict the differentiation degree of lung adenocarcinoma with an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78-0.89) in the internal validation cohort. The AUC of the nomogram and radiographic model was 0.86 (95% CI: 0.80-0.91), 0.78 (95% CI: 0.72-0.84) in the internal validation cohort respectively. The AUC of the nomogram in the external validation cohort was 0.73 (95% CI: 0.58-0.88). Delong's test showed that the nomogram performed better than radiographic features alone (p=0.001). CONCLUSIONS The proposed radiomics nomogram has the potential to predict the differentiation degree of lung adenocarcinoma preoperatively.
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Affiliation(s)
- Y Yang
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - M Tan
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - X Huang
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China
| | - L Tang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, China.
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90
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Kodama T, Arimura H, Shirakawa Y, Ninomiya K, Yoshitake T, Shioyama Y. Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy. Thorac Cancer 2022; 13:2117-2126. [PMID: 35711108 PMCID: PMC9346172 DOI: 10.1111/1759-7714.14483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/04/2022] [Indexed: 12/25/2022] Open
Abstract
Background This study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non‐small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR). Methods We divided 125 primary stage I NSCLC patients (LRR: 34, DM: 22) into training (n = 60) and test datasets (n = 65), and the training dataset was augmented to 260 cases using a synthetic minority oversampling technique. The relapse predictabilities of the conventional wavelet‐based features (WF), topology‐based features [BF, Betti number (BN) map features; iBF, inverted BN map features], and their combined features (BWF, iBWF) were compared. The patients were stratified into high‐risk and low‐risk groups using the medians of the radiomics scores in the training dataset. Results For the LRR in the test, the iBF, iBWF, and WF showed statistically significant differences (p < 0.05), and the highest nLPC was obtained for the iBF. For the DM in the test, the iBWF showed a significant difference and the highest nLPC. Conclusion The iBF indicated the potential of improving the LRR and DM prediction of stage I NSCLC patients prior to undergoing SABR.
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Affiliation(s)
- Takumi Kodama
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical SciencesKyushu UniversityFukuokaJapan
| | - Yuko Shirakawa
- National Hospital Organization Kyushu Cancer CenterFukuokaJapan
| | - Kenta Ninomiya
- Sanford Burnham Prebys Medical Discovery InstituteLa JollaCaliforniaUSA
| | - Tadamasa Yoshitake
- Department of Clinical Radiology, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
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91
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Wang L, Liu A, Wang Z, Xu N, Zhou D, Qu T, Liu G, Wang J, Yang F, Guo X, Chi W, Xue F. A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population. Front Oncol 2022; 12:816766. [PMID: 35774128 PMCID: PMC9237399 DOI: 10.3389/fonc.2022.816766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/11/2022] [Indexed: 12/21/2022] Open
Abstract
Background The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). Methods A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts. Results Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients' OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843-0.879 vs. C-index, 0.851, 95% CI: 0.832-0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841-0.896 vs. C-index, 0.854, 95% CI: 0.824-0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation. Conclusion The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.
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Affiliation(s)
- Lijie Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ailing Liu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Zhiheng Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Provincial Key Laboratory of Immunohematology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ning Xu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Dandan Zhou
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Tao Qu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Guiyuan Liu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jingtao Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Hematology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fujun Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xiaolei Guo
- The Department for Chronic and Non-Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Weiwei Chi
- National Administration of Health Data, Jinan, China
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
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92
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Nemoto T, Takeda A, Matsuo Y, Kishi N, Eriguchi T, Kunieda E, Kimura R, Sanuki N, Tsurugai Y, Yagi M, Aoki Y, Oku Y, Kimura Y, Han C, Shigematsu N. Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis-4N0M0 Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy. JCO Clin Cancer Inform 2022; 6:e2100176. [PMID: 35749675 PMCID: PMC9259118 DOI: 10.1200/cci.21.00176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Clear evidence indicating whether surgery or stereotactic body radiation therapy (SBRT) is best for non-small-cell lung cancer (NSCLC) is lacking. SBRT has many advantages. We used artificial neural networks (NNs) to predict treatment outcomes for patients with NSCLC receiving SBRT, aiming to aid in decision making. PATIENTS AND METHODS Among consecutive patients receiving SBRT between 2005 and 2019 in our institution, we retrospectively identified those with Tis-T4N0M0 NSCLC. We constructed two NNs for prediction of overall survival (OS) and cancer progression in the first 5 years after SBRT, which were tested using an internal and an external test data set. We performed risk group stratification, wherein 5-year OS and cancer progression were stratified into three groups. RESULTS In total, 692 patients in our institution and 100 patients randomly chosen in the external institution were enrolled. The NNs resulted in concordance indexes for OS of 0.76 (95% CI, 0.73 to 0.79), 0.68 (95% CI, 0.60 to 0.75), and 0.69 (95% CI, 0.61 to 0.76) and area under the curve for cancer progression of 0.80 (95% CI, 0.75 to 0.84), 0.72 (95% CI, 0.60 to 0.83), and 0.70 (95% CI, 0.57 to 0.81) in the training, internal test, and external test data sets, respectively. The survival and cumulative incidence curves were significantly stratified. NNs selected low-risk cancer progression groups of 5.6%, 6.9%, and 7.0% in the training, internal test, and external test data sets, respectively, suggesting that 48% of patients with peripheral Tis-4N0M0 NSCLC can be at low-risk for cancer progression. CONCLUSION Predictions of SBRT outcomes using NNs were useful for Tis-4N0M0 NSCLC. Our results are anticipated to open new avenues for NN predictions and provide decision-making guidance for patients and physicians.
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Affiliation(s)
- Takafumi Nemoto
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan.,Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Atsuya Takeda
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Yukinori Matsuo
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Noriko Kishi
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takahisa Eriguchi
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Etsuo Kunieda
- Radiation Oncology Center, Tokyo General Hospital, Tokyo, Japan
| | - Ryusei Kimura
- Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan
| | - Naoko Sanuki
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Yuichiro Tsurugai
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | | | - Yousuke Aoki
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Yohei Oku
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Yuto Kimura
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Changhee Han
- Department of Health Sciences, Saitama Prefectural University, Saitama, Japan
| | - Naoyuki Shigematsu
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
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93
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Lin L, Wu X, Jiang Y, Deng C, Luo X, Han J, Hu J, Cao X. Down-regulated NEDD4L facilitates tumor progression through activating Notch signaling in lung adenocarcinoma. PeerJ 2022; 10:e13402. [PMID: 35646490 PMCID: PMC9138047 DOI: 10.7717/peerj.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/17/2022] [Indexed: 01/14/2023] Open
Abstract
Neural precursor cell expressed developmentally down-regulated 4-like protein (NEDD4L), an E3 ubiquitin ligase, exerts an important role in diverse biological processes including development, tumorigenesis, and tumor progression. Although the role of NEDD4L in the pathogenesis of lung adenocarcinoma (LUAD) has been described, the mechanism by which NEDD4L promotes LUAD progression remains poorly understood. In the study, the correlation between NEDD4L level and clinical outcome in LUAD patients was analysed using the data from The Cancer Genome Atlas (TCGA) database. NEDD4L expression in LUAD cell lines and tissue samples was assessed through quantitative real-time PCR (qRT-PCR). The biological function of NEDD4L on regulating LUAD cell proliferation was tested with Cell Counting Kit-8 (CCK-8) assay in vitro, and mouse xenograft tumor model in vivo. We found that NEDD4L expression was significantly decreased in LUAD tissues and cell lines. Lower expression of NEDD4L exhibited a significantly poorer overall survival. Functionally, NEDD4L knockdown in H1299 cells accelerated cell growth, whereas NEDD4L overexpression in A549 cells repressed cell proliferation. NEDD4L overexpression also inhibited tumor xenograft growth in vivo. Mechanistically, NEDD4L decreased the protein stability of notch receptor 2 (Notch2) through facilitating its ubiquitination and degradation by ubiquitin-proteasome system. Consequently, NEDD4L negatively regulated Notch signaling activation in LUAD cells, and RO4929097 (a Notch inhibitor) treatment effectively repressed the effect of NEDD4L knockdown on LUAD cell proliferation. Taken together, these results demonstrate that down-regulated NEDD4L facilitates LUAD progression by activating Notch signaling, and NEDD4L may be a promising target to treat LUAD.
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Affiliation(s)
- Liping Lin
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
| | - Xuan Wu
- Department of Oncology, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province, China
| | - Yuanxue Jiang
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
| | - Caijiu Deng
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
| | - Xi Luo
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
| | - Jianjun Han
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
| | - Jiazhu Hu
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
| | - Xiaolong Cao
- Department of Oncology, Panyu Central Hospital, Guangzhou, Guangdong Province, China,Cancer Institute of Panyu, Guangzhou, Guangdong Province, China
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94
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Wang Y, Liu F, Mo Y, Huang C, Chen Y, Chen F, Zhang X, Yin Y, Liu Q, Zhang L. Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma. Thorac Cancer 2022; 13:1806-1813. [PMID: 35538917 PMCID: PMC9200880 DOI: 10.1111/1759-7714.14459] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 11/25/2022] Open
Abstract
Background To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. Methods A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. Results Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05). Conclusions The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.
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Affiliation(s)
- Yang Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Fang Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yan Mo
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Yingxin Chen
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Fuliang Chen
- Department of Thoracic Surgery, Chengxin Hospital, Yuncheng, Shandong, China
| | - Xiangwei Zhang
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Yunxin Yin
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Qiang Liu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Lin Zhang
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
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95
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Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma. DISEASE MARKERS 2022; 2022:2056837. [PMID: 35578691 PMCID: PMC9107363 DOI: 10.1155/2022/2056837] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/13/2022] [Accepted: 04/23/2022] [Indexed: 12/20/2022]
Abstract
Identifying an epidermal growth factor receptor (EGFR) mutation is important because EGFR tyrosine kinase inhibitors are the first-line treatment of choice for patients with EGFR mutation-positive lung adenocarcinomas (LUAC). This study is aimed at developing and validating a radiomics-based machine learning (ML) approach to identify EGFR mutations in patients with LUAC. We retrospectively collected data from 201 patients with positive EGFR mutation LUAC (140 in the training cohort and 61 in the validation cohort). We extracted 1316 radiomics features from preprocessed CT images and selected 14 radiomics features and 1 clinical feature which were most relevant to mutations through filter method. Subsequently, we built models using 7 ML approaches and established the receiver operating characteristic (ROC) curve to assess the discriminating performance of these models. In terms of predicting EGFR mutation, the model derived from radiomics features and combined models (radiomics features and relevant clinical factors) had an AUC of 0.79 (95% confidence interval (CI): 0.77-0.82), 0.86 (0.87-0.88), respectively. Our study offers a radiomics-based ML model using filter methods to detect the EGFR mutation in patients with LUAC. This convenient and low-cost method may be of help to noninvasively identify patients before obtaining tumor sample for molecule testing.
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96
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Zhang Q, Zhang Q, Liu T, Bao T, Li Q, Yang Y. Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study. Front Oncol 2022; 12:868164. [PMID: 35463357 PMCID: PMC9021381 DOI: 10.3389/fonc.2022.868164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background With advances in high-throughput computational mining techniques, various quantitative predictive models that are based on ultrasound have been developed. However, the lack of reproducibility and interpretability have hampered clinical use. In this study, we aimed at developing and validating an interpretable and simple-to-use US nomogram that is based on quantitative morphometric features for the prediction of breast malignancy. Methods Successive 917 patients with histologically confirmed breast lesions were included in this retrospective multicentric study and assigned to one training cohort and two external validation cohorts. Morphometric features were extracted from grayscale US images. After feature selection and validation of regression assumptions, a dynamic nomogram with a web-based calculator was developed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. Results Through feature selection, three morphometric features were identified as being the most optimal for predicting malignancy, and all regression assumptions of the prediction model were met. Combining all these predictors, the nomogram demonstrated a good discriminative performance in the training cohort and in the two external validation cohorts with AUCs of 0.885, 0.907, and 0.927, respectively. In addition, calibration and decision curves analyses showed good calibration and clinical usefulness. Conclusions By incorporating US morphometric features, we constructed an interpretable and easy-to-use dynamic nomogram for quantifying the probability of breast malignancy. The developed nomogram has good generalization abilities, which may fit into clinical practice and serve as a potential tool to guide personalized treatment. Our findings show that quantitative morphometric features from different ultrasound machines and systems can be used as imaging surrogate biomarkers for the development of robust and reproducible quantitative ultrasound dynamic models in breast cancer research.
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Affiliation(s)
- Qingling Zhang
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Qinglu Zhang
- Department of Ultrasonography, Shandong Provincial Third Hospital Affiliated to Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Taixia Liu
- Department of Ultrasonography, Linyi People's Hospital, Linyi, China
| | - Tingting Bao
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Qingqing Li
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - You Yang
- Depatment of Ultrasonography, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
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97
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Serum Level of CEACAM1 in Patients with Nonsmall Cell Lung Cancer and Its Clinical Significance in Cancer Tissue. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7948010. [PMID: 35449849 PMCID: PMC9018175 DOI: 10.1155/2022/7948010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/23/2022]
Abstract
Objective To analyze the expression of CEACAM1 in serum of patients with nonsmall cell lung cancer (NSCLC) and to explore the correlation and clinical significance between the expression of CEACAM1 and pathological parameters of NSCLC tissue. Methods A total of 100 patients with NSCLC who underwent tumor resection were screened. Another 100 healthy patients in physical examination department were selected as control group. Venous blood and cancer tissue samples were collected. The expression of CEACAM1, TGF-β, VEGF-A, and IL-8 was detected. Results The results of various indicators in the lung cancer group were much higher than those in the healthy group; CEACAM1 was significantly positively correlated with TGF-β expression. The later the clinical stage and the higher the degree of differentiation of cancer tissue specimens, the more the expression of CEACAM1 in serum samples. Conclusions The expression level of CEACAM1 in the serum of NSCLC patients is strongly correlated with TGF-β, VEGF-A, and IL-8, indicating that serum CEACAM1 and TGF-β levels can predict the occurrence, progression, and prognosis of lung cancer and provide a target for future targeted therapy of lung cancer.
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98
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Pérez-Morales J, Lu H, Mu W, Tunali I, Kutuk T, Eschrich SA, Balagurunathan Y, Gillies RJ, Schabath MB. Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers. Cancer Biomark 2022; 33:489-501. [DOI: 10.3233/cbm-210194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: Image-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes. METHODS: Data and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes. RESULTS: Decision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS). CONCLUSION: We utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.
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Affiliation(s)
- Jaileene Pérez-Morales
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hong Lu
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Wei Mu
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Tugce Kutuk
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Steven A. Eschrich
- Department of Bioinformatics and Biostatistics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Yoganand Balagurunathan
- Department of Bioinformatics and Biostatistics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Robert J. Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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99
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Lysdahlgaard S. Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study. Radiography (Lond) 2022; 28:718-724. [PMID: 35428570 DOI: 10.1016/j.radi.2022.03.015] [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: 01/23/2022] [Revised: 03/07/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Liver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset. METHODS The Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy. RESULTS The performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%-99.91%) to 100.00% (95% CI: 86.77%-100.00%), 91.30% (95% CI: 71.96%-98.93%) to 100.00% (95% CI: 83.89%-100.00%)and 94.00% (95% CI: 83.45%-98.75%) to 100.00% (95% CI: 92.45%-100.00%), respectively. CONCLUSION ML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT. IMPLICATIONS FOR PRACTICE Differentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.
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Affiliation(s)
- S Lysdahlgaard
- Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Imaging Research Initiative Southwest (IRIS), Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark.
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100
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Yang Y, Xu L, Sun L, Zhang P, Farid SS. Machine learning application in personalised lung cancer recurrence and survivability prediction. Comput Struct Biotechnol J 2022; 20:1811-1820. [PMID: 35521553 PMCID: PMC9043969 DOI: 10.1016/j.csbj.2022.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 12/24/2022] Open
Abstract
Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.
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Key Words
- ANNs, artificial neural networks
- ANOVA, analysis of variance
- AUC, the area under the ROC curve
- CART, classification and regression tree
- CNV, copy number variation
- DTs, decision trees
- Decision tree
- FFNN, Feedforward neural networks
- LS-SVM, least-squares support vector machine
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- Lung cancer
- ML, machine learning
- Machine learning
- NSCLC, non-small cell lung cancer
- Personalized diagnosis and prognosis
- ROC, receiver operating characteristic
- SVMs, support vector machines
- TCGA, The Cancer Genome Atlas
- TNM, a common cancer staging system while T, N and M refers to tumour, node and metastasis
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Affiliation(s)
- Yang Yang
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
| | - Li Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200043, China
| | - Liangdong Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200043, China
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200043, China
| | - Suzanne S. Farid
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
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