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Zheng R, Wang X, Zhu L, Yan R, Li J, Wei Y, Zhang F, Du H, Guo L, He Y, Shi H, Han A. A deep learning method for predicting the origins of cervical lymph node metastatic cancer on digital pathological images. iScience 2024; 27:110645. [PMID: 39252964 PMCID: PMC11381752 DOI: 10.1016/j.isci.2024.110645] [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: 03/08/2024] [Revised: 06/15/2024] [Accepted: 07/30/2024] [Indexed: 09/11/2024] Open
Abstract
The metastatic cancer of cervical lymph nodes presents complex shapes and poses significant challenges for doctors in determining its origin. We established a deep learning framework to predict the status of lymph nodes in patients with cervical lymphadenopathy (CLA) by hematoxylin and eosin (H&E) stained slides. This retrospective study utilized 1,036 cervical lymph node biopsy specimens at the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU). A multiple-instance learning algorithm designed for key region identification was applied, and cross-validation experiments were conducted in the dataset. Additionally, the model distinguished between primary lymphoma and metastatic cancer with high prediction accuracy. We also validated our model and other models on an external dataset. Our model showed better generalization and achieved the best results on both internal and external datasets. This algorithm offers an approach for evaluating cervical lymph node status before surgery, significantly aiding physicians in preoperative diagnosis and treatment planning.
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Affiliation(s)
- Runliang Zheng
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Xuenian Wang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Lianghui Zhu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Renao Yan
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Jiawen Li
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Yani Wei
- Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Fenfen Zhang
- Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Hong Du
- Department of Pathology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Linlang Guo
- Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yonghong He
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Huijuan Shi
- Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Anjia Han
- Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
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Zhang S, Wang X, Yang Z, Ding M, Zhang M, Young KH, Zhang X. Minimal residual disease detection in lymphoma: methods, procedures and clinical significance. Front Immunol 2024; 15:1430070. [PMID: 39188727 PMCID: PMC11345172 DOI: 10.3389/fimmu.2024.1430070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/22/2024] [Indexed: 08/28/2024] Open
Abstract
Lymphoma is a highly heterogeneous lymphohematopoietic tumor. As our understanding of the biological and pathological characteristics of lymphoma improves, we are identifying an increasing number of lymphoma subtypes. Genotyping has enhanced our ability to diagnose, treat, and monitor the prognosis of lymphoma. Despite significant improvements in treatment effectiveness, traditional methods for assessing disease response and monitoring prognosis are imperfect, and there is no significant improvement in overall remission rates for lymphoma patients. Minimal Residual Disease (MRD) is often indicative of refractory disease or early relapse. For lymphoma patients, personalized MRD monitoring techniques offer an efficient means to estimate disease remission levels, predict early relapse risk, and assess the effectiveness of new drug regimens. In this review, we delve into the MRD procedures in lymphoma, including sample selection and requirements, detection methods and their limitations and advantages, result interpretation. Besides, we also introduce the clinical applications of MRD detection in lymphoma.
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Affiliation(s)
- Sijun Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou, Henan, China
| | - Xiangyu Wang
- School of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhenzhen Yang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou, Henan, China
| | - Mengjie Ding
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou, Henan, China
| | - Mingzhi Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou, Henan, China
| | - Ken H. Young
- Division of Hematopathology, Duke University Medicine Center, Duke Cancer Institute, Durham, NC, United States
| | - Xudong Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou, Henan, China
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Lue KH, Chen YH, Chu SC, Lin CB, Wang TF, Liu SH. Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study. Ann Nucl Med 2024; 38:647-658. [PMID: 38704786 DOI: 10.1007/s12149-024-01936-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. METHODS We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. RESULTS In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively). CONCLUSIONS Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.
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Affiliation(s)
- Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, No.880, Sec.2, Chien-kuo Rd., Hualien, 970302, Taiwan
| | - Yu-Hung Chen
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, No.880, Sec.2, Chien-kuo Rd., Hualien, 970302, Taiwan.
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No.707, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan.
- School of Medicine, College of Medicine, Tzu Chi University, No.701, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan.
| | - Sung-Chao Chu
- School of Medicine, College of Medicine, Tzu Chi University, No.701, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan
- Department of Hematology and Oncology, Buddhist Tzu Chi Medical Foundation, Hualien Tzu Chi Hospital, Hualien, Taiwan
| | - Chih-Bin Lin
- School of Medicine, College of Medicine, Tzu Chi University, No.701, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan
- Department of Internal Medicine, Buddhist Tzu Chi Medical Foundation, Hualien Tzu Chi Hospital, Hualien, Taiwan
| | - Tso-Fu Wang
- School of Medicine, College of Medicine, Tzu Chi University, No.701, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan
- Department of Hematology and Oncology, Buddhist Tzu Chi Medical Foundation, Hualien Tzu Chi Hospital, Hualien, Taiwan
| | - Shu-Hsin Liu
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, No.880, Sec.2, Chien-kuo Rd., Hualien, 970302, Taiwan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No.707, Sec.3, Zhongyang Rd, Hualien, 970473, Taiwan
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Dondi F, Gatta R, Treglia G, Piccardo A, Albano D, Camoni L, Gatta E, Cavadini M, Cappelli C, Bertagna F. Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review. Rev Endocr Metab Disord 2024; 25:175-186. [PMID: 37434097 PMCID: PMC10808150 DOI: 10.1007/s11154-023-09822-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/30/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting. METHODS A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases. RESULTS Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques. CONCLUSION Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.
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Affiliation(s)
- Francesco Dondi
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, Brescia, 25123, Italy
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, Brescia, Italy
| | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland
| | | | - Domenico Albano
- Nuclear Medicine, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Luca Camoni
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, Brescia, 25123, Italy
| | - Elisa Gatta
- Unit of Endocrinology and Metabolism, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Maria Cavadini
- Unit of Endocrinology and Metabolism, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Carlo Cappelli
- Unit of Endocrinology and Metabolism, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy
| | - Francesco Bertagna
- Nuclear Medicine, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, Brescia, 25123, Italy.
- Nuclear Medicine, ASST Spedali Civili di Brescia and Università degli Studi di Brescia, Brescia, Italy.
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Nakajo M, Jinguji M, Ito S, Tani A, Hirahara M, Yoshiura T. Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology. Jpn J Radiol 2024; 42:28-55. [PMID: 37526865 PMCID: PMC10764437 DOI: 10.1007/s11604-023-01476-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.
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Affiliation(s)
- Masatoyo Nakajo
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Megumi Jinguji
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Soichiro Ito
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atushi Tani
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuho Hirahara
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Wang R, Zhang Z, Zhao M, Zhu G. A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach. Technol Cancer Res Treat 2024; 23:15330338241277389. [PMID: 39267420 PMCID: PMC11456957 DOI: 10.1177/15330338241277389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/07/2024] [Accepted: 07/29/2024] [Indexed: 09/17/2024] Open
Abstract
Through meticulous examination of lymph nodes, the stage and severity of cancer can be determined. This information is invaluable for doctors to select the most appropriate treatment plan and predict patient prognosis; however, any oversight in the examination of lymph nodes may lead to cancer metastasis and poor prognosis. In this review, we summarize a significant number of articles supported by statistical data and clinical experience, proposing a standardized evaluation protocol for lymph nodes. This protocol begins with preoperative imaging to assess the presence of lymph node metastasis. Radiomics has replaced the single-modality approach, and deep learning models have been constructed to assist in image analysis with superior performance to that of the human eye. The focus of this review lies in intraoperative lymphadenectomy. Multiple international authorities have recommended specific numbers for lymphadenectomy in various cancers, providing surgeons with clear guidelines. These numbers are calculated by applying various statistical methods and real-world data. In the third chapter, we mention the growing concern about immune impairment caused by lymph node dissection, as the lack of CD8 memory T cells may have a negative impact on postoperative immunotherapy. Both excessive and less lymph node dissection have led to conflicting findings on postoperative immunotherapy. In conclusion, we propose a protocol that can be referenced by surgeons. With the systematic management of lymph nodes, we can control tumor progression with the greatest possible likelihood, optimize the preoperative examination process, reduce intraoperative risks, and improve postoperative quality of life.
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Affiliation(s)
- Ruochong Wang
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zhiyan Zhang
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Mengyun Zhao
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Guiquan Zhu
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Xu C, Feng J, Yue Y, Cheng W, He D, Qi S, Zhang G. A hybrid few-shot multiple-instance learning model predicting the aggressiveness of lymphoma in PET/CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107872. [PMID: 37922655 DOI: 10.1016/j.cmpb.2023.107872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 09/29/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Patients with aggressive non-Hodgkin lymphoma (NHL) undergo distinct therapy strategies compared with indolent NHL patients. However, it is challenging to estimate NHL aggressiveness based on visual inspection of positron emission tomography (PET) or computed tomography (CT) images. Since diffuse large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL) are the most typical and dominant aggressive and indolent NHL, respectively, this study aims to develop an artificial-intelligence-enabled model to distinguish DLBCL from FL in PET/CT images as the first step to tackle this challenge. METHODS We propose a hybrid few-shot multiple-instance learning model to predict the aggressiveness of the NHL. First, rotation-based self-supervision learning (SSL) has been employed to train the encoder on a large-scale, publicly available CT image dataset. Second, hybrid instance-level features are obtained for each NHL lesion by combining deep features with the radiomics features from both PET and CT modalities. Third, instance-level features are transformed into bag-level (or patient-level) representations. Finally, bag-level representations are fed into a distance-based classifier through few-shot learning to predict NHL aggressiveness. RESULTS Our model achieves an accuracy of 0.751 ± 0.008, a sensitivity of 0.787 ± 0.012, a specificity of 0.715 ± 0.013, an F1-score of 0.753 ± 0.009, and an area under the curve (AUC) of 0.795 ± 0.009 at the bag level. It outperforms the typical counterparts that use the radiomic features, random forest for feature selection, and support vector machines (SVMs) as classifiers. The three counterparts yield accuracies of 0.714 ± 0.023, 0.705 ± 0.008, and 0.698 ± 0.008, respectively. Moreover, settings of the SSL training dataset (Deep lesion) and task (rotation), hybrid CT and radiomic PET features, the pool-layer strategy of maximum, and distance-based classifier generate the best model. CONCLUSIONS A hybrid few-shot multiple-instance learning model can predict lymphoma aggressiveness in PET/CT images and could be a potential tool for determining therapy strategies. Hybrid features and the combination of SSL, few-shot learning, and weakly supervised learning are the two powerful pillars of the model, and these can be expanded to other medical applications with limited samples and incomplete annotations.
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Affiliation(s)
- Caiwen Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Jie Feng
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wanjun Cheng
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Guojun Zhang
- Department of Hematology, Shengjing Hospital of China Medical University, Shenyang, China.
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Lue KH, Chen YH, Chu SC, Chang BS, Lin CB, Chen YC, Lin HH, Liu SH. A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study. Nucl Med Commun 2023; 44:1094-1105. [PMID: 37728592 DOI: 10.1097/mnm.0000000000001776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
OBJECTIVE The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable lung adenocarcinoma varies, and their use across different generations of PET machines has not been thoroughly investigated. We compared handcrafted radiomics and deep learning using different PET scanners to predict pN+ in resectable lung adenocarcinoma. METHODS We retrospectively analyzed pretreatment 18 F-FDG PET from 148 lung adenocarcinoma patients who underwent curative surgery. Patients were separated into analog (n = 131) and digital (n = 17) PET cohorts. Handcrafted radiomics and a ResNet-50 deep-learning model of the primary tumor were used to predict pN+ status. Models were trained in the analog PET cohort, and the digital PET cohort was used for cross-scanner validation. RESULTS In the analog PET cohort, entropy, a handcrafted radiomics, independently predicted pN+. However, the areas under the receiver-operating-characteristic curves (AUCs) and accuracy for entropy were only 0.676 and 62.6%, respectively. The ResNet-50 model demonstrated a better AUC and accuracy of 0.929 and 94.7%, respectively. In the digital PET validation cohort, the ResNet-50 model also demonstrated better AUC (0.871 versus 0.697) and accuracy (88.2% versus 64.7%) than entropy. The ResNet-50 model achieved comparable specificity to visual interpretation but with superior sensitivity (83.3% versus 66.7%) in the digital PET cohort. CONCLUSION Applying deep learning across different generations of PET scanners may be feasible and better predict pN+ than handcrafted radiomics. Deep learning may complement visual interpretation and facilitate tailored therapeutic strategies for resectable lung adenocarcinoma.
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Affiliation(s)
- Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology,
| | - Yu-Hung Chen
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology,
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
- School of Medicine, College of Medicine, Tzu Chi University,
| | - Sung-Chao Chu
- School of Medicine, College of Medicine, Tzu Chi University,
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
| | - Bee-Song Chang
- Department of Cardiothoracic Surgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
| | - Chih-Bin Lin
- Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
| | - Yen-Chang Chen
- School of Medicine, College of Medicine, Tzu Chi University,
- Department of Anatomical Pathology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien,
| | - Hsin-Hon Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan and
- Department of Nuclear Medicine, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Shu-Hsin Liu
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology,
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation,
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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10
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Wei X, Guo H, Yu J, Liu Y, Zhao Y, He X. Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107711. [PMID: 37451228 DOI: 10.1016/j.cmpb.2023.107711] [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] [Received: 01/01/2023] [Revised: 06/24/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Bioluminescence tomography (BLT) is a noninvasive optical imaging technique that provides qualitative and quantitative information on the spatial distribution of tumors in living animals. Researchers have proposed a list of algorithms and strategies for BLT reconstruction to improve its reconstruction quality. However, multi-target BLT reconstruction remains challenging in practical clinical applications due to the mutual interference of optical signals and difficulty in source separation. METHODS To solve this problem, this study proposes the subspace decision optimization (SDO) approach based on the traditional iterative permissible region strategy. The SDO approach transforms a single permissible region into multiple subspaces by clustering analysis. These subspaces are shrunk based on subspace shrinking optimization to achieve spatial continuity of the permissible regions. In addition, these subspaces are merged to construct a new permissible region and then the next iteration of reconstruction is carried out to ensure the stability of the results. Finally, all the iterative results are optimized based on the normal distribution model and the distribution properties of the targets to ensure the sparsity of each target and the non-biasing of the overall results. RESULTS Experimental results show that the SDO approach can automatically identify and separate different targets, ensuring the accuracy and quality of multi-target BLT reconstruction results. Meanwhile, SDO can combine various types of reconstruction algorithms and provide stable and high-quality reconstruction results independent of the algorithm parameters. CONCLUSIONS The SDO approach provides an integrated solution to the multi-target BLT reconstruction problem, realizing the whole process including target recognition, separation, reconstruction, and result enhancement, which can extend the application domain of BLT.
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Affiliation(s)
- Xiao Wei
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China
| | - Hongbo Guo
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China.
| | - Jingjing Yu
- The School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China
| | - Yanqiu Liu
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China
| | - Yingcheng Zhao
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China
| | - Xiaowei He
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China.
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11
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Hamdi M, Senan EM, Jadhav ME, Olayah F, Awaji B, Alalayah KM. Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas. Diagnostics (Basel) 2023; 13:2258. [PMID: 37443652 DOI: 10.3390/diagnostics13132258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/10/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models.
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Affiliation(s)
- Mohammed Hamdi
- Department of Computer Science, Faculty of Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | - Mukti E Jadhav
- Shri Shivaji Science & Arts College, Chikhli Dist., Buldana 443201, India
| | - Fekry Olayah
- Department of Information System, Faculty Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
| | - Bakri Awaji
- Department of Computer Science, Faculty of Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
| | - Khaled M Alalayah
- Department of Computer Science, Faculty of Science and Arts, Sharurah, Najran University, Najran 66462, Saudi Arabia
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12
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Veziroglu EM, Farhadi F, Hasani N, Nikpanah M, Roschewski M, Summers RM, Saboury B. Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma. Semin Nucl Med 2023; 53:426-448. [PMID: 36870800 DOI: 10.1053/j.semnuclmed.2022.11.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 03/06/2023]
Abstract
Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.
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Affiliation(s)
| | - Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Geisel School of Medicine at Dartmouth, Hanover, NH
| | - Navid Hasani
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Moozhan Nikpanah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Department of Radiology, University of Alabama at Birmingham, AL
| | - Mark Roschewski
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - Ronald M Summers
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD.
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