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Kang B, Chen S, Wang G, Huang Y, Wu H, He J, Li X, Xi G, Wu G, Zhuo S. Ovarian cancer identification technology based on deep learning and second harmonic generation imaging. JOURNAL OF BIOPHOTONICS 2024:e202400200. [PMID: 38955356 DOI: 10.1002/jbio.202400200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024]
Abstract
Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.
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Affiliation(s)
- Bingzi Kang
- School of Science, Jimei University, Xiamen, China
| | - Siyu Chen
- College of Computer Engineering, Jimei University, Xiamen, China
| | | | - Yuhang Huang
- School of Science, Jimei University, Xiamen, China
| | - Han Wu
- School of Science, Jimei University, Xiamen, China
| | - Jiajia He
- School of Science, Jimei University, Xiamen, China
| | - Xiaolu Li
- School of Science, Jimei University, Xiamen, China
| | - Gangqin Xi
- School of Science, Jimei University, Xiamen, China
| | - Guizhu Wu
- Department of Gynecology, Obstetrics and Gynecology Hospital, School of Medicine, Tongji University, Shanghai, China
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Chen J, Liu L, He Z, Su D, Liu C. CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:180-195. [PMID: 38343232 DOI: 10.1007/s10278-023-00903-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/12/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Lei Liu
- School of Computer Science and Engineering, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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Jiang W, Wang H, Chen W, Zhao Y, Yan B, Chen D, Dong X, Cheng J, Lin Z, Zhuo S, Wang H, Yan J. Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer. Bioeng Transl Med 2023; 8:e10526. [PMID: 37206212 PMCID: PMC10189440 DOI: 10.1002/btm2.10526] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 05/21/2023] Open
Abstract
The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagenDL classifier, rather than low-collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.
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Affiliation(s)
- Wei Jiang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
- School of ScienceJimei UniversityXiamenFujianPeople's Republic of China
| | - Huaiming Wang
- Department of Colorectal Surgery & Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, the Sixth Affiliated Hospital, Supported by National Key Clinical DisciplineSun Yat‐sen UniversityGuangzhouGuangdongPeople's Republic of China
| | - Weisheng Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
| | - Yandong Zhao
- Department of Pathology, the Sixth Affiliated HospitalSun Yat‐sen UniversityGuangzhouGuangdongPeople's Republic of China
| | - Botao Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
| | - Dexin Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
| | - Xiaoyu Dong
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
| | - Jiaxin Cheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
| | - Zexi Lin
- School of ScienceJimei UniversityXiamenFujianPeople's Republic of China
| | - Shuangmu Zhuo
- School of ScienceJimei UniversityXiamenFujianPeople's Republic of China
| | - Hui Wang
- Department of Colorectal Surgery & Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, the Sixth Affiliated Hospital, Supported by National Key Clinical DisciplineSun Yat‐sen UniversityGuangzhouGuangdongPeople's Republic of China
| | - Jun Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical MedicineSouthern Medical UniversityGuangzhouPeople's Republic of China
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Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning. J Clin Med 2023; 12:jcm12041599. [PMID: 36836131 PMCID: PMC9967588 DOI: 10.3390/jcm12041599] [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: 01/11/2023] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/19/2023] Open
Abstract
An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice.
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Jiang W, Wang H, Zheng J, Zhao Y, Xu S, Zhuo S, Wang H, Yan J. Post-operative anastomotic leakage and collagen changes in patients with rectal cancer undergoing neoadjuvant chemotherapy vs chemoradiotherapy. Gastroenterol Rep (Oxf) 2022; 10:goac058. [PMID: 36324613 PMCID: PMC9619829 DOI: 10.1093/gastro/goac058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 06/24/2022] [Accepted: 09/28/2022] [Indexed: 11/04/2022] Open
Abstract
Background A significant difference in the anastomotic leakage (AL) rate has been observed between patients with locally advanced rectal cancer who have undergone preoperative chemotherapy and those undergoing preoperative chemoradiotherapy. This study aimed to quantitatively analyse collagen structural changes caused by preoperative chemoradiotherapy and illuminate the relationship between collagen changes and AL. Methods Anastomotic distal and proximal "doughnut" specimens from the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were quantitatively assessed for collagen structural changes between patients with and without preoperative radiotherapy using multiphoton imaging. Then, patients treated with preoperative chemoradiotherapy were used as a training cohort to construct an AL-SVM classifier by the Mann-Whitney U test and support vector machine (SVM). An independent test cohort from the Fujian Province Cancer Hospital (Fuzhou, China) was used to validate the AL-SVM classifier. Results A total of 207 patients were included from the Sixth Affiliated Hospital of Sun Yat-sen University. The AL rate in the preoperative chemoradiotherapy group (n = 107) was significantly higher than that in the preoperative chemotherapy group (n = 100) (21.5% vs 7.0%, P = 0.003). A fully quantitative analysis showed notable morphological and spatial distribution feature changes in collagen in the preoperative chemoradiotherapy group. Then, the patients who received preoperative chemoradiotherapy were used as a training cohort to construct the AL-SVM classifier based on five collagen features and the tumor distance from the anus. The AL-SVM classifier showed satisfactory discrimination and calibration with areas under the curve of 0.907 and 0.856 in the training and test cohorts, respectively. Conclusions The collagen structure may be notably altered by preoperative radiotherapy. The AL-SVM classifier was useful for the individualized prediction of AL in rectal cancer patients undergoing preoperative chemoradiotherapy.
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Affiliation(s)
| | | | | | - Yandong Zhao
- Department of Pathology, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Shuoyu Xu
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, P. R. China,Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
| | - Shuangmu Zhuo
- Corresponding authors. Jun Yan, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China. Tel: +86-20-61641682; Fax: +86-20-61641683; ; Hui Wang, Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Er Heng Rd, Guangzhou, Guangdong 510655, P. R. China. Tel: +86-20-61641682; Fax: +86-20-61641683; ; Shuangmu Zhuo, School of Science, Jimei University, Xiamen, Fujian 361021, P. R. China. Tel.: +86-592-6181893; Fax: +86-592-6181893;
| | - Hui Wang
- Corresponding authors. Jun Yan, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China. Tel: +86-20-61641682; Fax: +86-20-61641683; ; Hui Wang, Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Er Heng Rd, Guangzhou, Guangdong 510655, P. R. China. Tel: +86-20-61641682; Fax: +86-20-61641683; ; Shuangmu Zhuo, School of Science, Jimei University, Xiamen, Fujian 361021, P. R. China. Tel.: +86-592-6181893; Fax: +86-592-6181893;
| | - Jun Yan
- Corresponding authors. Jun Yan, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China. Tel: +86-20-61641682; Fax: +86-20-61641683; ; Hui Wang, Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Er Heng Rd, Guangzhou, Guangdong 510655, P. R. China. Tel: +86-20-61641682; Fax: +86-20-61641683; ; Shuangmu Zhuo, School of Science, Jimei University, Xiamen, Fujian 361021, P. R. China. Tel.: +86-592-6181893; Fax: +86-592-6181893;
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Scalco E, Rizzo G, Gomez-Flores W. Automatic Feature Construction Based on Genetic Programming for Survival Prediction in Lung Cancer Using CT Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3797-3800. [PMID: 36085831 DOI: 10.1109/embc48229.2022.9871039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the radiomics workflow, machine learning builds classification models from a set of input features. However, some features can be irrelevant and redundant, reducing the classification performance. This paper proposes using the Genetic Programming (GP) algorithm to automatically construct a reduced number of independent and relevant radiomic features. The proposed method is applied to patients affected by Non-Small Cell Lung Cancer (NSCLC) with pre-operative computed tomography (CT) images to predict the two-year survival by the use of linear classifiers. The model built using GP features is compared with benchmark models built using traditional features. The use of the GP algorithm increased classification performance: [Formula: see text] for the proposed model vs. [Formula: see text] and 0.64 for the benchmark models. Hence, the proposed approach better stratifies patients at high and low risk according to their overall postoperative survival time.
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Jiang W, Wang S, Wan J, Zheng J, Dong X, Liu Z, Wang G, Xu S, Xiao W, Gao Y, Zhuo S, Yan J. Association of the Collagen Signature with Pathological Complete Response in Rectal Cancer Patients. Cancer Sci 2022; 113:2409-2424. [PMID: 35485874 PMCID: PMC9277261 DOI: 10.1111/cas.15385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/20/2022] [Accepted: 04/24/2022] [Indexed: 11/28/2022] Open
Abstract
Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.
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Affiliation(s)
- Wei Jiang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China.,School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Shijie Wang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jinliang Wan
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jixiang Zheng
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Xiaoyu Dong
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Zhangyuanzhu Liu
- Department of Hepatobiliary and Pancreatic Surgery, Guangdong Provincial Hospital of Traditional Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Guangxing Wang
- School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Shuoyu Xu
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China
| | - Weiwei Xiao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, 510060, China
| | - Yuanhong Gao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, 510060, China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Jun Yan
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
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Wang G, Sun Y, Jiang S, Wu G, Liao W, Chen Y, Lin Z, Liu Z, Zhuo S. Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images. BIOMEDICAL OPTICS EXPRESS 2021; 12:5658-5669. [PMID: 34692207 PMCID: PMC8515988 DOI: 10.1364/boe.429918] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 05/30/2023]
Abstract
Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.
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Affiliation(s)
- Guangxing Wang
- School of Science, Jimei University, Xiamen 361021, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China
- These authors contributed equally
| | - Yang Sun
- Department of Gynecology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou 350014, China
- These authors contributed equally
| | - Shuisen Jiang
- School of Science, Jimei University, Xiamen 361021, China
| | - Guizhu Wu
- Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 200030, China
| | - Wenliang Liao
- School of Science, Jimei University, Xiamen 361021, China
| | - Yuwei Chen
- Department of Gynecology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Zexi Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine of the Ministry of Education & Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Zhiyi Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen 361021, China
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Jiang W, Li M, Tan J, Feng M, Zheng J, Chen D, Liu Z, Yan B, Wang G, Xu S, Xiao W, Gao Y, Zhuo S, Yan J. A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients. Ann Surg Oncol 2021; 28:6408-6421. [PMID: 34148136 DOI: 10.1245/s10434-021-10218-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/09/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients. METHODS In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort. RESULTS The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT. CONCLUSIONS The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.
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Affiliation(s)
- Wei Jiang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.,School of Science, Jimei University, Xiamen, Fujian, People's Republic of China
| | - Min Li
- Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China
| | - Jie Tan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Mingyuan Feng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Jixiang Zheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Dexin Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Zhangyuanzhu Liu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Botao Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Guangxing Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, Fujian, People's Republic of China
| | - Shuoyu Xu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.,Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Weiwei Xiao
- Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China
| | - Yuanhong Gao
- Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China.
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, Fujian, People's Republic of China.
| | - Jun Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
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10
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Nougaret S, McCague C, Tibermacine H, Vargas HA, Rizzo S, Sala E. Radiomics and radiogenomics in ovarian cancer: a literature review. Abdom Radiol (NY) 2021; 46:2308-2322. [PMID: 33174120 DOI: 10.1007/s00261-020-02820-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/01/2020] [Accepted: 10/10/2020] [Indexed: 01/25/2023]
Abstract
Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.
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Affiliation(s)
- S Nougaret
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France. .,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France.
| | - Cathal McCague
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Hichem Tibermacine
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France.,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, CH, Switzerland.,Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Lugano, CH, Switzerland
| | - E Sala
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
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11
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Xiong F, Wang Y, You T, Li HH, Fu TT, Tan H, Huang W, Jiang Y. The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT. Medicine (Baltimore) 2021; 100:e25307. [PMID: 33761733 PMCID: PMC9282117 DOI: 10.1097/md.0000000000025307] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 03/06/2021] [Indexed: 01/05/2023] Open
Abstract
In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.
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Affiliation(s)
- Fei Xiong
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Ye Wang
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Tao You
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Han han Li
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Ting ting Fu
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Huibin Tan
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Weicai Huang
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Yuanliang Jiang
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
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