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Baniasadi A, Das JP, Prendergast CM, Beizavi Z, Ma HY, Jaber MY, Capaccione KM. Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis. J Transl Med 2024; 22:567. [PMID: 38872212 PMCID: PMC11177383 DOI: 10.1186/s12967-024-05379-1] [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: 02/11/2024] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.
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Affiliation(s)
- Alireza Baniasadi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
| | - Jeeban P Das
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Conor M Prendergast
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Zahra Beizavi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Hong Y Ma
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | | | - Kathleen M Capaccione
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
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Yang L, Zhao X, Yang L, Chang Y, Cao C, Li X, Wang Q, Song Z. A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes. Sci Rep 2024; 14:9596. [PMID: 38671007 PMCID: PMC11053028 DOI: 10.1038/s41598-024-60198-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676-0.785) for the training, 0.701 (95% CI 0.630-0.773) for internal validation, and 0.813 (95% CI 0.734-0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.
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Affiliation(s)
- Liu Yang
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Xueyi Zhao
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Lixian Yang
- Department of Breast Surgery, Xingtai People's Hospital, Xingtai, 054000, China
| | - Yan Chang
- Department of Breast Surgery, Affiliated Hospital of Hebei Engineering University, Handan, 056000, China
| | - Congbo Cao
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Xiaolong Li
- Department of Breast Surgery, The Fourth Hospital of Shijiazhuang, Shijiazhuang, 050000, China
| | - Quanle Wang
- Department of Breast Surgery, The Fourth Hospital of Shijiazhuang, Shijiazhuang, 050000, China
| | - Zhenchuan Song
- Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
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Zhang S, Li H, Jing Q, Shen W, Luo W, Dai R. Anesthesia decision analysis using a cloud-based big data platform. Eur J Med Res 2024; 29:201. [PMID: 38528564 DOI: 10.1186/s40001-024-01764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/01/2024] [Indexed: 03/27/2024] Open
Abstract
Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.
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Affiliation(s)
- Shuiting Zhang
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Hui Li
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Qiancheng Jing
- Department of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South China, Changsha, 410000, Hunan, China
| | - Weiyun Shen
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Wei Luo
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Ruping Dai
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China.
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Liu H, Zou L, Xu N, Shen H, Zhang Y, Wan P, Wen B, Zhang X, He Y, Gui L, Kong W. Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer. NPJ Breast Cancer 2024; 10:22. [PMID: 38472210 PMCID: PMC10933422 DOI: 10.1038/s41523-024-00628-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.
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Affiliation(s)
- Han Liu
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Liwen Zou
- Department of Mathematics, Nanjing University, Nanjing, 210008, China
| | - Nan Xu
- Department of Ultrasound, Jinling Hospital, Medical School of Nanjing University/General Hospital of Eastern Theater Command, Nanjing, 210002, China
| | - Haiyun Shen
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Yu Zhang
- Department of Mathematics, Nanjing University, Nanjing, 210008, China
| | - Peng Wan
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China
| | - Baojie Wen
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Xiaojing Zhang
- Department of Ultrasound, Taizhou Hospital Affiliated to Nanjing University of Chinese Medicine, Taizhou, 225300, China
| | - Yuhong He
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Luying Gui
- School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Wentao Kong
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
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Yao J, Zhou W, Zhu Y, Zhou J, Chen X, Zhan W. Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer. Oncol Lett 2024; 27:95. [PMID: 38288042 PMCID: PMC10823315 DOI: 10.3892/ol.2024.14228] [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: 08/10/2023] [Accepted: 12/19/2023] [Indexed: 01/31/2024] Open
Abstract
Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.
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Affiliation(s)
- Jiejie Yao
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Xiaosong Chen
- Comprehensive Breast Health Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
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You J, Huang Y, Ouyang L, Zhang X, Chen P, Wu X, Jin Z, Shen H, Zhang L, Chen Q, Pei S, Zhang B, Zhang S. Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study. EClinicalMedicine 2024; 69:102499. [PMID: 38440400 PMCID: PMC10909626 DOI: 10.1016/j.eclinm.2024.102499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 03/06/2024] Open
Abstract
Background Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).
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Affiliation(s)
- Jingjing You
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Yue Huang
- Department of Ultrasound, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lizhu Ouyang
- Department of Ultrasound, Shunde Hospital of Southern Medical University, Foshan, Guangdong, China
| | - Xiao Zhang
- School of Information Science and Technology, Northwest University, Xi’an, China
| | - Pei Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xuewei Wu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Zhe Jin
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Hui Shen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shufang Pei
- Department of Ultrasound, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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Wang Q, Lin Y, Ding C, Guan W, Zhang X, Jia J, Zhou W, Liu Z, Bai G. Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography. Eur Radiol 2024:10.1007/s00330-024-10638-2. [PMID: 38337068 DOI: 10.1007/s00330-024-10638-2] [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: 05/22/2023] [Revised: 12/05/2023] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVES We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.
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Affiliation(s)
- Qian Wang
- Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China
| | - Cong Ding
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wenting Guan
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China
| | - Jianye Jia
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wei Zhou
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Ziyan Liu
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Genji Bai
- Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China.
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
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Wang M, Liu Z, Ma L. Application of artificial intelligence in ultrasound imaging for predicting lymph node metastasis in breast cancer: A meta-analysis. Clin Imaging 2024; 106:110048. [PMID: 38065024 DOI: 10.1016/j.clinimag.2023.110048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND This study aims to comprehensively evaluate the accuracy and effectiveness of ultrasound imaging based on artificial intelligence algorithms in predicting lymph node metastasis in breast cancer patients through a meta-analysis. METHODS We systematically searched PubMed, Embase, and Cochrane Library for literature published up to May 2023. The search terms included artificial intelligence, ultrasound, breast cancer, and lymph node. Studies meeting the inclusion criteria were selected, and data were extracted for analysis. The main evaluation indicators included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC). The heterogeneity was assessed using the Cochrane Q test combined with the I^2 statistic expressing the percentage of total effect variation that can be attributed to the effect variation between studies, as recommended by the Cochrane Handbook for heterogeneity quantification. A threshold p-value of 0.10 was considered to compensate for the low power of the Q test. Sensitivity analysis was performed to assess the stability of individual studies, and publication bias was determined with funnel plots. Additionally, fagan plots were used to assess clinical utility. RESULTS Ten studies involving 4726 breast cancer patients were included in the meta-analysis. The results showed that ultrasound imaging based on artificial intelligence algorithms had high accuracy and effectiveness in predicting lymph node metastasis in breast cancer patients. The pooled sensitivity was 0.88 (95% CI: 0.81-0.93; P < 0.001; I2 = 84.68), specificity was 0.75 (95% CI: 0.66-0.83; P < 0.001; I2 = 91.11), and AUC was 0.89 (95% CI: 0.86-0.91). The positive likelihood ratio was 3.5 (95% CI: 2.6-4.8), negative likelihood ratio was 0.16 (95% CI: 0.10-0.26), and diagnostic odds ratio was 23 (95% CI: 13-40). However, the combined sensitivity of ultrasound imaging based on non-AI algorithms for predicting lymph node metastasis in breast cancer patients was 0.78 (95%CI: 0.63-0.88), the specificity was 0.76 (95%CI: 0.63-0.86), and the AUC was 0.84 (95%CI: 0.80-0.87). The positive likelihood ratio was 3.3 (95% CI: 1.9-5.6), the negative likelihood ratio was 0.29 (95% CI: 0.15-0.54), and the diagnostic odds ratio was 11 (95% CI: 4-33). Due to limited sample size (n = 2), meta-analysis was not conducted for the outcome of predicting lymph node metastasis burden. CONCLUSION Ultrasound imaging based on artificial intelligence algorithms holds promise in predicting lymph node metastasis in breast cancer patients, demonstrating high accuracy and effectiveness. The application of this technology helps in the diagnosis and treatment decisions for breast cancer patients and is expected to become an important tool in future clinical practice.
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Affiliation(s)
- Minghui Wang
- Department of Breast Surgery, Affiliate Hospital of Chengde Medical University, Hebei 067000, China
| | - Zihui Liu
- Department of Pathology, Affiliate Hospital of Chengde Medical University, Hebei 067000, China
| | - Lihui Ma
- Department of Breast Surgery, Affiliate Hospital of Chengde Medical University, Hebei 067000, China.
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Yu H, Li Q, Xie F, Wu S, Chen Y, Huang C, Xu Y, Niu Q. A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer. Acta Radiol 2024; 65:185-194. [PMID: 38115683 DOI: 10.1177/02841851231215464] [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] [Indexed: 12/21/2023]
Abstract
BACKGROUND It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. PURPOSE To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. MATERIAL AND METHODS This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. RESULTS Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. CONCLUSION The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.
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Affiliation(s)
- Haitong Yu
- Medical Imaging Department, Weifang Medical University, Weifang, Shandong, PR China
| | - Qin Li
- Department of Radiology, WeiFang Traditional Chinese Hospital, Weifang, Shandong, PR China
| | - Fucai Xie
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, PR China
| | - Shasha Wu
- Department of Radiology, WeiFang Traditional Chinese Hospital, Weifang, Shandong, PR China
| | - Yongsheng Chen
- Department of Radiology, WeiFang Traditional Chinese Hospital, Weifang, Shandong, PR China
| | - Chuansheng Huang
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, PR China
| | - Yonglin Xu
- Department of Computer Science, Shanghai University, People's Republic of China
| | - Qingliang Niu
- Department of Radiology, WeiFang Traditional Chinese Hospital, Weifang, Shandong, PR China
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10
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Shahriarirad R, Meshkati Yazd SM, Fathian R, Fallahi M, Ghadiani Z, Nafissi N. Prediction of sentinel lymph node metastasis in breast cancer patients based on preoperative features: a deep machine learning approach. Sci Rep 2024; 14:1351. [PMID: 38228684 PMCID: PMC10791698 DOI: 10.1038/s41598-024-51244-y] [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: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024] Open
Abstract
Sentinel lymph node (SLN) biopsy is the standard surgical approach to detect lymph node metastasis in breast cancer. Machine learning is a novel tool that provides better accuracy for predicting positive SLN involvement in breast cancer patients. This study obtained data from 2890 surgical cases of breast cancer patients from two referral hospitals in Iran from 2000 to 2021. Patients whose SLN involvement status was identified were included in our study. The dataset consisted of preoperative features, including patient features, gestational factors, laboratory data, and tumoral features. In this study, TabNet, an end-to-end deep learning model, was proposed to predict SLN involvement in breast cancer patients. We compared the accuracy of our model with results from logistic regression analysis. A total of 1832 patients with an average age of 51 ± 12 years were included in our study, of which 697 (25.5%) had SLN involvement. On average, the TabNet model achieved an accuracy of 75%, precision of 81%, specificity of 70%, sensitivity of 87%, and AUC of 0.74, while the logistic model demonstrated an accuracy of 70%, precision of 73%, specificity of 65%, sensitivity of 79%, F1 score of 73%, and AUC of 0.70 in predicting the SLN involvement in patients. Vascular invasion, tumor size, core needle biopsy pathology, age, and FH had the most contributions to the TabNet model. The TabNet model outperformed the logistic regression model in all metrics, indicating that it is more effective in predicting SLN involvement in breast cancer patients based on preoperative data.
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Affiliation(s)
- Reza Shahriarirad
- Thoracic and Vascular Surgery Research Center, Shiraz University of Medical Science, Shiraz, Iran
| | | | - Ramin Fathian
- Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | | | - Zahra Ghadiani
- Department of Breast, Rasoul Akram Hospital Clinical Research Development Center (RCRDC), Iran University of Medical Sciences, Tehran, Iran
| | - Nahid Nafissi
- Department of Breast, Rasoul Akram Hospital Clinical Research Development Center (RCRDC), Iran University of Medical Sciences, Tehran, Iran.
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Ge L, Wu J, Jin Y, Xu D, Wang Z. Noninvasive Assessment of Tumor Histological Grade in Invasive Breast Carcinoma Based on Ultrasound Radiomics and Clinical Characteristics: A Multicenter Study. Technol Cancer Res Treat 2024; 23:15330338241257424. [PMID: 38780506 PMCID: PMC11119369 DOI: 10.1177/15330338241257424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/16/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.
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Affiliation(s)
- Lifang Ge
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Jiangfeng Wu
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Yun Jin
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Dong Xu
- Department of Ultrasonography, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [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: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Lyu S, Zhang M, Zhang B, Zhu J, Gao L, Qiu Y, Yang L, Zhang Y. The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1536-1543. [PMID: 37712556 DOI: 10.1002/jcu.23556] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Female breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging. OBJECTIVE The purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC. METHODS 302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray-scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad-score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model. RESULTS When we selected 10 radiomics features to calculate the rad-score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value. CONCLUSION The radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).
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Affiliation(s)
- Shuyi Lyu
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
- Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, Zhejiang, China
| | - Meiwu Zhang
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
| | - Baisong Zhang
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
| | - Jiazhen Zhu
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
| | - Libo Gao
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
| | - Yuqin Qiu
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
| | - Liu Yang
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
| | - Yan Zhang
- Department of Ultrasound, Ningbo No. 2 Hospital, Zhejiang, China
- Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, Zhejiang, China
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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15
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Dai Q, Tao Y, Liu D, Zhao C, Sui D, Xu J, Shi T, Leng X, Lu M. Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis. Front Oncol 2023; 13:1261080. [PMID: 38023240 PMCID: PMC10643192 DOI: 10.3389/fonc.2023.1261080] [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: 07/18/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Objective This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC). Methods In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared. Results The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases. Conclusion The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.
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Affiliation(s)
- Quan Dai
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| | - Yi Tao
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
| | - Dongmei Liu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Chen Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Dong Sui
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Jinshun Xu
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| | - Tiefeng Shi
- Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaoping Leng
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Man Lu
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
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Zheng B, Chen Q. Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer. BMC Med Imaging 2023; 23:135. [PMID: 37723421 PMCID: PMC10506204 DOI: 10.1186/s12880-023-01090-7] [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: 06/09/2023] [Accepted: 08/29/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Whether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the treatment plan. OBJECTIVE Constructed a predict model based on ultrasound examination, which forest axillary lymph node metastasis in breast cancer, and validated this model. METHOD Patients admitted to Xiamen First Hospital from April 2018 to August 2021 with complete case data were included in this study. Patients who had undergone breast cancer resection and axillary lymph node dissection or sentinel lymph node biopsy were divided into a training and validation cohort in a 7:3 ratio. In the training cohort, patients were divided into metastatic and non-metastatic groups based on whether axillary lymph nodes had metastasis. The parameters of the two groups were compared, and statistically significant parameters were included in multivariate analysis. Then, a Nomogram model was constructed, named Lymph metastasis predict model (LMPM). Calibration curves, receiver operating curve (ROC), and decision curve analysis (DCA) were plotted between the training and validation cohort, calculate the risk score of each patient, identify the optimal cutoff value, and test the predictive efficacy of LMPM. RESULT Two hundred seventy-three patients were enrolled in final study, the average age 49.7 ± 8.7, training cohort included 191 patients, the diameter of breast cancer, the lymph node peak systolic flow velocity (LNPS) and the cortex area hilum ratio (CH) of lymph node were exist significant difference in metastatic and non-metastatic group. Multivariate analysis showed cancer diameter, LNPS and CH included in LMPM, the cutoff value was 95, the calibration curve, ROC, DCA in training and validation cohort show satisfactory result. CONCLUSION The predict model-LMPM, can predict axillary lymph node metastasis in breast cancer, which is useful for developing personalized treatment plans. However, further validation of the model is required by incorporating a larger number of patients.
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Affiliation(s)
- Biyu Zheng
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, No. 55, Zhenhai Road, Siming District, Xiamen, Fujian, China.
| | - Qingshuang Chen
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, No. 55, Zhenhai Road, Siming District, Xiamen, Fujian, China
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Li WB, Du ZC, Liu YJ, Gao JX, Wang JG, Dai Q, Huang WH. Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning. Front Oncol 2023; 13:1219838. [PMID: 37719009 PMCID: PMC10503049 DOI: 10.3389/fonc.2023.1219838] [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: 05/09/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Objective To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients. Methods A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models. Results Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review. Conclusion A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
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Affiliation(s)
- Wei-Bin Li
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Ultrasonic Medicine Affiliated Hospital of Xizang Minzu University, Xianyang, China
| | - Zhi-Cheng Du
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yue-Jie Liu
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jun-Xue Gao
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jia-Gang Wang
- Department of Ultrasonic Medicine of Shantou Central Hospital, Shantou, China
| | - Qian Dai
- School of Informatics, Xiamen University, Xiamen, China
| | - Wen-He Huang
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Champendal M, Marmy L, Malamateniou C, Sá Dos Reis C. Artificial intelligence to support person-centred care in breast imaging - A scoping review. J Med Imaging Radiat Sci 2023; 54:511-544. [PMID: 37183076 DOI: 10.1016/j.jmir.2023.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023]
Abstract
AIM To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies. METHODS The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage. RESULTS 3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used. CONCLUSIONS The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
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Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Laurent Marmy
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Christina Malamateniou
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, UK.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
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Pesapane F, Mariano L, Magnoni F, Rotili A, Pupo D, Nicosia L, Bozzini AC, Penco S, Latronico A, Pizzamiglio M, Corso G, Cassano E. Future Directions in the Assessment of Axillary Lymph Nodes in Patients with Breast Cancer. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1544. [PMID: 37763661 PMCID: PMC10534800 DOI: 10.3390/medicina59091544] [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: 07/18/2023] [Revised: 08/15/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Background and Objectives: Breast cancer (BC) is a leading cause of morbidity and mortality worldwide, and accurate assessment of axillary lymph nodes (ALNs) is crucial for patient management and outcomes. We aim to summarize the current state of ALN assessment techniques in BC and provide insights into future directions. Materials and Methods: This review discusses various imaging techniques used for ALN evaluation, including ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography. It highlights advancements in these techniques and their potential to improve diagnostic accuracy. The review also examines landmark clinical trials that have influenced axillary management, such as the Z0011 trial and the IBCSG 23-01 trial. The role of artificial intelligence (AI), specifically deep learning algorithms, in improving ALN assessment is examined. Results: The review outlines the key findings of these trials, which demonstrated the feasibility of avoiding axillary lymph node dissection (ALND) in certain patient populations with low sentinel lymph node (SLN) burden. It also discusses ongoing trials, including the SOUND trial, which investigates the use of axillary ultrasound to identify patients who can safely avoid sentinel lymph node biopsy (SLNB). Furthermore, the potential of emerging techniques and the integration of AI in enhancing ALN assessment accuracy are presented. Conclusions: The review concludes that advancements in ALN assessment techniques have the potential to improve patient outcomes by reducing surgical complications while maintaining accurate disease staging. However, challenges such as standardization of imaging protocols and interpretation criteria need to be addressed. Future research should focus on large-scale clinical trials to validate emerging techniques and establish their efficacy and cost-effectiveness. Over-all, this review provides valuable insights into the current status and future directions of ALN assessment in BC, highlighting opportunities for improving patient care.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Luciano Mariano
- Breast Imaging Division, AOU Città della Scienza e della Salute di Torino, 10126 Turin, Italy;
| | - Francesca Magnoni
- Division of Breast Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (F.M.); (G.C.)
- European Cancer Prevention Organization (ECP), 20122 Milan, Italy
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Davide Pupo
- Radiology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Giovanni Corso
- Division of Breast Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (F.M.); (G.C.)
- European Cancer Prevention Organization (ECP), 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
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20
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Wu P, Jiang Y, Xing H, Song W, Cui X, Wu XL, Xu G. Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study. Phys Med Biol 2023; 68:175023. [PMID: 37524093 DOI: 10.1088/1361-6560/acec2d] [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: 04/18/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.
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Affiliation(s)
- Peiyan Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Yan Jiang
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Hanshuo Xing
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Wenbo Song
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xing Long Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
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21
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Xiu Y, Jiang C, Zhang S, Yu X, Qiao K, Huang Y. Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning. World J Surg Oncol 2023; 21:244. [PMID: 37563717 PMCID: PMC10416453 DOI: 10.1186/s12957-023-03109-3] [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: 02/11/2023] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients. METHODS From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared. RESULTS NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706). CONCLUSIONS The ML model XGBoost can well predict NSLNM in breast cancer patients.
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Affiliation(s)
- Yuting Xiu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Cong Jiang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Shiyuan Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Xiao Yu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Kun Qiao
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, China.
| | - Yuanxi Huang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, China.
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22
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Cuccurullo V, Rapa M, Catalfamo B, Cascini GL. Role of Nuclear Sentinel Lymph Node Mapping Compared to New Alternative Imaging Methods. J Pers Med 2023; 13:1219. [PMID: 37623469 PMCID: PMC10455335 DOI: 10.3390/jpm13081219] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/22/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
With the emergence of sentinel node technology, many patients can be staged histopathologically using lymphatic mapping and selective lymphadenectomy. Structural imaging by using US, CT and MR permits precise measurement of lymph node volume, which is strongly associated with neoplastic involvement. Sentinel lymph node detection has been an ideal field of application for nuclear medicine because anatomical data fails to represent the close connections between the lymphatic system and regional lymph nodes, or, more specifically, to identify the first draining lymph node. Hybrid imaging has demonstrated higher accuracy than standard imaging in SLN visualization on images, but it did not change in terms of surgical detection. New alternatives without ionizing radiations are emerging now from "non-radiological" fields, such as ophthalmology and dermatology, where fluorescence or opto-acoustic imaging, for example, are widely used. In this paper, we will analyze the advantages and limits of the main innovative methods in sentinel lymph node detection, including innovations in lymphoscintigraphy techniques that persist as the gold standard to date.
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Affiliation(s)
- Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80138 Napoli, Italy
| | - Marco Rapa
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80138 Napoli, Italy
| | - Barbara Catalfamo
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy (G.L.C.)
| | - Giuseppe Lucio Cascini
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy (G.L.C.)
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23
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Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023; 15:3139. [PMID: 37370748 PMCID: PMC10296633 DOI: 10.3390/cancers15123139] [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: 05/03/2023] [Revised: 06/02/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.
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Affiliation(s)
- Humayra Afrin
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
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24
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Vrdoljak J, Krešo A, Kumrić M, Martinović D, Cvitković I, Grahovac M, Vickov J, Bukić J, Božic J. The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review. Cancers (Basel) 2023; 15:cancers15082400. [PMID: 37190328 DOI: 10.3390/cancers15082400] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023] Open
Abstract
Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.
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Affiliation(s)
- Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Ante Krešo
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Marko Kumrić
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Dinko Martinović
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Ivan Cvitković
- Department of Surgery, University Hospital of Split, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, 21000 Split, Croatia
| | - Josip Vickov
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
| | - Josipa Bukić
- Department of Pharmacy, University of Split School of Medicine, 21000 Split, Croatia
| | - Joško Božic
- Department of Pathophysiology, University of Split School of Medicine, 21000 Split, Croatia
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25
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Lin F, Li Q, Wang Z, Shi Y, Ma H, Zhang H, Zhang K, Yang P, Zhang R, Duan S, Gu Y, Mao N, Xie H. Intratumoral and peritumoral radiomics for preoperatively predicting the axillary non-sentinel lymph node metastasis in breast cancer on the basis of contrast-enhanced mammography: a multicenter study. Br J Radiol 2023; 96:20220068. [PMID: 36542866 PMCID: PMC9975381 DOI: 10.1259/bjr.20220068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 11/07/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To develop and test a contrast-enhanced mammography (CEM)-based radiomics model using intratumoral and peritumoral regions to predict non-sentinel lymph node (NSLN) metastasis in breast cancer before surgery. METHODS This multicenter study included 365 breast cancer patients with sentinel lymph node metastasis. Intratumoral regions of interest (ROIs) were manually delineated, and peritumoral ROIs (5 and 10 mm) were automatically obtained. Five models, including intratumoral model, peritumoral (5 and 10 mm) models, and intratumoral+peritumoral (5 and 10 mm) models, were constructed by support vector machine classifier on the basis of optimal features selected by variance threshold, SelectKbest, and least absolute shrinkage and selection operator algorithms. The predictive performance of radiomics models was evaluated by receiver operating characteristic curves. An external testing set was used to test the model. The Memorial Sloan Kettering Cancer Center (MSKCC) model was used to compare the predictive performance with radiomics model. RESULTS The intratumoral ROI and intratumoral+peritumoral 10-mm ROI-based radiomics model achieved the best performance with an area under the curve (AUC) of 0.8000 (95% confidence interval [CI]: 0.6871-0.8266) in the internal testing set. In the external testing set, the AUC of radiomics model was 0.7567 (95% CI: 0.6717-0.8678), higher than that of MSKCC model (AUC = 0.6681, 95% CI: 0.5148-0.8213) (p = 0.361). CONCLUSIONS The intratumoral and peritumoral radiomics based on CEM had an acceptable predictive performance in predicting NSLN metastasis in breast cancer, which could be seen as a supplementary predicting tool to help clinicians make appropriate surgical plans. ADVANCES IN KNOWLEDGE The intratumoral and peritumoral CEM-based radiomics model could noninvasively predict NSLN metastasis in breast cancer patients before surgery.
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Affiliation(s)
- Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Qin Li
- Department of Radiology, WeiFang Traditional Chinese Hospital, Weifang, China
| | - Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ran Zhang
- Huiying Medical Technology, Beijing, China
| | | | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | | | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
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26
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Mao N, Zhang H, Dai Y, Li Q, Lin F, Gao J, Zheng T, Zhao F, Xie H, Xu C, Ma H. Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study. Br J Cancer 2023; 128:793-804. [PMID: 36522478 PMCID: PMC9977865 DOI: 10.1038/s41416-022-02092-y] [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: 05/01/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM. METHODS Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience. RESULTS The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance. CONCLUSIONS Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance.
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Affiliation(s)
- Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Yi Dai
- Department of Radiology, Peking University Shenzhen Hospital, 518000, Shenzhen, P. R. China
| | - Qin Li
- Department of Radiology, Fudan University Cancer Center, 200433, Shanghai, P. R. China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Tiantian Zheng
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, 264005, Yantai, Shandong, P. R. China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Cong Xu
- Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China.
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China.
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27
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Windsor GO, Bai H, Lourenco AP, Jiao Z. Application of artificial intelligence in predicting lymph node metastasis in breast cancer. FRONTIERS IN RADIOLOGY 2023; 3:928639. [PMID: 37492388 PMCID: PMC10364981 DOI: 10.3389/fradi.2023.928639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 01/31/2023] [Indexed: 07/27/2023]
Abstract
Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.
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Affiliation(s)
- Gabrielle O. Windsor
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - Harrison Bai
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Ana P. Lourenco
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - Zhicheng Jiao
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
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28
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Jia Y, Wu R, Lu X, Duan Y, Zhu Y, Ma Y, Nie F. Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study. Cancers (Basel) 2023; 15:cancers15030838. [PMID: 36765796 PMCID: PMC9913836 DOI: 10.3390/cancers15030838] [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: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/27/2023] [Indexed: 02/01/2023] Open
Abstract
This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making.
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Affiliation(s)
- Yingying Jia
- Ultrasound Medical Center, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Ultrasonography, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Ruichao Wu
- School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730030, China
| | - Xiangyu Lu
- School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730030, China
| | - Ying Duan
- Department of Ultrasound, Gansu Provincial Cancer Hospital, West Lake East Street No. 2, Qilihe District, Lanzhou 730030, China
| | - Yangyang Zhu
- Ultrasound Medical Center, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Ultrasonography, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Yide Ma
- School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730030, China
- Correspondence: (Y.M.); (F.N.)
| | - Fang Nie
- Ultrasound Medical Center, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Ultrasonography, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Correspondence: (Y.M.); (F.N.)
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Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment. Cancers (Basel) 2023; 15:cancers15030634. [PMID: 36765592 PMCID: PMC9913601 DOI: 10.3390/cancers15030634] [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: 12/14/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Due to recent changes in breast cancer treatment strategy, significantly more patients are treated with neoadjuvant systemic therapy (NST). Radiological methods do not precisely determine axillary lymph node status, with up to 30% of patients being misdiagnosed. Hence, supplementary methods for lymph node status assessment are needed. This study aimed to apply and evaluate machine learning models on clinicopathological data, with a focus on patients meeting NST criteria, for lymph node metastasis prediction. METHODS From the total breast cancer patient data (n = 8381), 719 patients were identified as eligible for NST. Machine learning models were applied for the NST-criteria group and the total study population. Model explainability was obtained by calculating Shapley values. RESULTS In the NST-criteria group, random forest achieved the highest performance (AUC: 0.793 [0.713, 0.865]), while in the total study population, XGBoost performed the best (AUC: 0.762 [0.726, 0.795]). Shapley values identified tumor size, Ki-67, and patient age as the most important predictors. CONCLUSION Tree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.
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Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review. Diagnostics (Basel) 2022; 13:diagnostics13010058. [PMID: 36611350 PMCID: PMC9818181 DOI: 10.3390/diagnostics13010058] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer. METHODS Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach. RESULTS A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy. CONCLUSIONS US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.
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Chen D, Liu X, Hu C, Hao R, Wang O, Xiao Y. Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis. Future Oncol 2022:1-14. [PMID: 36475996 DOI: 10.2217/fon-2022-0333] [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: 03/31/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022] Open
Abstract
Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.
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Affiliation(s)
- Danxiang Chen
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Xia Liu
- Department of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Chunlei Hu
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Rutian Hao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Ouchen Wang
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Yanling Xiao
- Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
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Gong X, Guo Y, Zhu T, Peng X, Xing D, Zhang M. Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1046005. [PMID: 36518318 PMCID: PMC9742555 DOI: 10.3389/fonc.2022.1046005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/11/2022] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer. MATERIALS AND METHODS Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity. RESULTS A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05). CONCLUSION Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.
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Affiliation(s)
| | | | | | | | | | - Minguang Zhang
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Li C, Zhang H, Chen J, Shao S, Li X, Yao M, Zheng Y, Wu R, Shi J. Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer. Clin Hemorheol Microcirc 2022:CH221608. [PMID: 36373313 DOI: 10.3233/ch-221608] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC). METHODS: A total of 97 patients with SA and 100 patients with BC were included in this study. The best model for classification was selected from among four different convolutional neural network (CNN) models, including Vgg16, Resnet18, Resnet50, and Desenet121. The intra-/inter-class correlation coefficient and least absolute shrinkage and selection operator method were used for radiomics feature selection. The clinical features selected were patient age and nodule size. The overall accuracy, sensitivity, specificity, Youden index, positive predictive value, negative predictive value, and area under curve (AUC) value were calculated for comparison of diagnostic efficacy. RESULTS: All the CNN models combined with radiomics and clinical data were significantly superior to CNN models only. The Desenet121+radiomics+clinical data model showed the best classification performance with an accuracy of 86.80%, sensitivity of 87.60%, specificity of 86.20% and AUC of 0.915, which was better than that of the CNN model only, which had an accuracy of 85.23%, sensitivity of 85.48%, specificity of 85.02%, and AUC of 0.870. In comparison, the diagnostic accuracy, sensitivity, specificity, and AUC value for breast radiologists were 72.08%, 100%, 43.30%, and 0.716, respectively. CONCLUSIONS: A combination of the CNN-radiomics model and clinical data could be a helpful auxiliary diagnostic tool for distinguishing between SA and BC.
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Affiliation(s)
- Chunxiao Li
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Huili Zhang
- School of Communication and Information Engineering, Shanghai University, Baoshan District, Shanghai, China
| | - Jing Chen
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Sihui Shao
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Xin Li
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Minghua Yao
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Yi Zheng
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Rong Wu
- Department of Ultra sound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, China
| | - Jun Shi
- School of Communication and Information Engineering, Shanghai University, Baoshan District, Shanghai, China
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Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions. Methods Protoc 2022; 5:mps5050078. [PMID: 36287050 PMCID: PMC9611546 DOI: 10.3390/mps5050078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 12/05/2022] Open
Abstract
Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).
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Zhu Y, Meng Z, Fan X, Duan Y, Jia Y, Dong T, Wang Y, Song J, Tian J, Wang K, Nie F. Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy. BMC Med 2022; 20:269. [PMID: 36008835 PMCID: PMC9410737 DOI: 10.1186/s12916-022-02469-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Accurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of expertise and reliable medical history. This study aimed to develop a deep learning (DL) radiomics model based on B-mode and color Doppler ultrasound images for assisting radiologists to improve their diagnoses of the etiology of unexplained CLA. METHODS Patients with unexplained CLA who received ultrasound examinations from three hospitals located in underdeveloped areas of China were retrospectively enrolled. They were all pathologically confirmed with reactive hyperplasia, tuberculous lymphadenitis, lymphoma, or metastatic carcinoma. By mimicking the diagnosis logic of radiologists, three DL sub-models were developed to achieve the primary diagnosis of benign and malignant, the secondary diagnosis of reactive hyperplasia and tuberculous lymphadenitis in benign candidates, and of lymphoma and metastatic carcinoma in malignant candidates, respectively. Then, a CLA hierarchical diagnostic model (CLA-HDM) integrating all sub-models was proposed to classify the specific etiology of each unexplained CLA. The assistant effectiveness of CLA-HDM was assessed by comparing six radiologists between without and with using the DL-based classification and heatmap guidance. RESULTS A total of 763 patients with unexplained CLA were enrolled and were split into the training cohort (n=395), internal testing cohort (n=171), and external testing cohorts 1 (n=105) and 2 (n=92). The CLA-HDM for diagnosing four common etiologies of unexplained CLA achieved AUCs of 0.873 (95% CI: 0.838-0.908), 0.837 (95% CI: 0.789-0.889), and 0.840 (95% CI: 0.789-0.898) in the three testing cohorts, respectively, which was systematically more accurate than all the participating radiologists. With its assistance, the accuracy, sensitivity, and specificity of six radiologists with different levels of experience were generally improved, reducing the false-negative rate of 2.2-10% and the false-positive rate of 0.7-3.1%. CONCLUSIONS Multi-cohort testing demonstrated our DL model integrating dual-modality ultrasound images achieved accurate diagnosis of unexplained CLA. With its assistance, the gap between radiologists with different levels of experience was narrowed, which is potentially of great significance for benefiting CLA patients in underdeveloped countries and regions worldwide.
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Affiliation(s)
- Yangyang Zhu
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.,CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Fan
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China
| | - Yin Duan
- Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, China
| | - Yingying Jia
- Department of Ultrasound, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
| | - Tiantian Dong
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China
| | - Yanfang Wang
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China
| | - Juan Song
- Department of Ultrasound, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
| | - Fang Nie
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China. .,Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China. .,Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China.
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Gu J, Jiang T. Ultrasound radiomics in personalized breast management: Current status and future prospects. Front Oncol 2022; 12:963612. [PMID: 36059645 PMCID: PMC9428828 DOI: 10.3389/fonc.2022.963612] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/01/2022] [Indexed: 11/18/2022] Open
Abstract
Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.
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Affiliation(s)
- Jionghui Gu
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
- Zhejiang University Cancer Center, Hangzhou, China
| | - Tian'an Jiang
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
- Zhejiang University Cancer Center, Hangzhou, China
- *Correspondence: Tian'an Jiang,
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Shen WQ, Guo Y, Ru WE, Li C, Zhang GC, Liao N, Du GQ. Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image. Front Oncol 2022; 12:850515. [PMID: 35719907 PMCID: PMC9204315 DOI: 10.3389/fonc.2022.850515] [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: 01/07/2022] [Accepted: 04/11/2022] [Indexed: 11/30/2022] Open
Abstract
Background The detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images. Materials and Methods We retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models. Results The proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models. Conclusion The ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.
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Affiliation(s)
- Wen-Qian Shen
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Department of Ultrasound, The Second Affifiliated Hospital of Harbin Medical University, Harbin, China
| | - Yanhui Guo
- Department of Computer Science, University of Illinois Springfield, Springfield, IL, United States
| | - Wan-Er Ru
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,College of Medicine, Shantou University, Shantou, China
| | - Cheukfai Li
- Department of Breast Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Guo-Chun Zhang
- Department of Breast Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ning Liao
- Department of Breast Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Guo-Qing Du
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Sun C, Li B, Wei G, Qiu W, Li D, Li X, Liu X, Wei W, Wang S, Liu Z, Tian J, Liang L. Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106914. [PMID: 35640390 DOI: 10.1016/j.cmpb.2022.106914] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 05/15/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage. However, outcomes are varied even among patients receiving similar treatments. We aimed to develop a prognostic signature to stratify outcomes and benefit from different chemotherapy regimens by analyzing whole slide images (WSI) using deep learning. METHODS We proposed an unsupervised deep learning network (variational autoencoder and generative adversarial network) in 180,819 image tiles from the training set (147 patients) to develop a WSI signature for predicting the disease-free survival (DFS) and overall survival (OS) of patients, and tested in validation set of 63 patients. An integrated nomogram was constructed to investigate the incremental value of deep learning signature (DLS) to TNM stage for individualized outcomes prediction. RESULTS The DLS was associated with DFS and OS in both training and validation sets and proved to be an independent prognostic factor. Integrating the DLS and clinicopathologic factors showed better performance (C-index: DFS, 0.748; OS, 0.794; in the validation set) than TNM stage. In patients whose DLS and clinical risk levels were inconsistent, their risk of relapse was reclassified. In the subgroup of patients treated with 3 months, high-DLS was associated with worse DFS (hazard ratio: 3.622-7.728). CONCLUSIONS The proposed based-WSI DLS improved risk stratification and could help identify patients with stage III CRC who may benefit from the prolonged duration of chemotherapy.
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Affiliation(s)
- Caixia Sun
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of and Engineering Medicine, Beihang University, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bingbing Li
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China
| | - Genxia Wei
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China
| | - Weihao Qiu
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China
| | - Danyi Li
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China
| | - Xiangzhao Li
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China
| | - Xiangyu Liu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Wei Wei
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Shuo Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of and Engineering Medicine, Beihang University, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100080, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of and Engineering Medicine, Beihang University, Beijing 100191, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Li Liang
- Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China.
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Fang Y, Kang D, Guo W, Zhang Q, Xu S, Huang X, Xi G, He J, Wu S, Li L, Han X, Chen J, Zheng L, Wang C, Chen J. Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy. JOURNAL OF BIOPHOTONICS 2022; 15:e202100365. [PMID: 35084104 DOI: 10.1002/jbio.202100365] [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: 11/25/2021] [Revised: 01/16/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.
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Affiliation(s)
- Ye Fang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Deyong Kang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wenhui Guo
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Qingyuan Zhang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Shuoyu Xu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, China
| | - Xingxin Huang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Gangqin Xi
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Jiajia He
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Shulian Wu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Lianhuang Li
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Xiahui Han
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Jianhua Chen
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Liqin Zheng
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Chuan Wang
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
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Liu Y, Ye F, Wang Y, Zheng X, Huang Y, Zhou J. Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer. Front Oncol 2022; 12:845334. [PMID: 35651796 PMCID: PMC9148964 DOI: 10.3389/fonc.2022.845334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/12/2022] [Indexed: 01/02/2023] Open
Abstract
Background This study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features. Methods A retrospective analysis of 281 patients with pathologically confirmed breast cancer was performed between January 2015 and March 2018. All patients were randomly divided into a training cohort (n = 197) and a validation cohort (n = 84). Univariate and multivariable logistic regression analyses were performed to identify the clinically important predictors of ALNM when developin1 g the nomogram. The area under the curve (AUC), calibration plots, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the nomogram. Results In univariate and multivariate analyses, lymphovascular invasion (LVI), axillary lymph node (ALN) cortex thickness, and an obliterated ALN fatty hilum were identified as independent predictors and integrated to develop a nomogram for predicting ALNM. The nomogram showed favorable sensitivity for ALNM with AUCs of 0.87 (95% confidence interval (CI), 0.81–0.92) and 0.84 (95% CI, 0.73–0.92) in the training and validation cohorts, respectively. The calibration plots of the nomogram showed good agreement between the nomogram prediction and actual ALNM diagnosis (P > 0.05). Decision curve analysis (DCA) revealed the net benefit of the nomogram. Conclusions This study developed a nomogram based on three daily available clinical parameters, with good accuracy and clinical utility, which may help the radiologist in decision-making for ultrasound-guided fine needle aspiration cytology/biopsy (US-FNAC/B) according to the nomogram score.
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Affiliation(s)
- Yubo Liu
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Feng Ye
- Department of Breast Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Breast Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yun Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xueyi Zheng
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yini Huang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Ultrasound, Sun Yat-Sen University Cancer Center, Guangzhou, China
- *Correspondence: Jianhua Zhou,
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Wu X, Guo Y, Sa Y, Song Y, Li X, Lv Y, Xing D, Sun Y, Cong Y, Yu H, Jiang W. Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer. Front Oncol 2022; 12:823897. [PMID: 35615151 PMCID: PMC9125761 DOI: 10.3389/fonc.2022.823897] [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: 11/28/2021] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs).Materials and MethodsBreast cancer patients with 1–2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden.ResultsA total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71–0.99] in the testing set and 0.82 (95% CI: 0.67–0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66–0.97) in the testing set and 0.77 (95% CI: 0.62–0.93) in the temporal validation cohort.DiscussionCESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.
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Affiliation(s)
- Xiaoqian Wu
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yu Guo
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yu Sa
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yipeng Song
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China
| | - Xinghua Li
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China
| | - Yongbin Lv
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Yan Sun
- Department of Otorhinolaryngology–Head and Neck Surgery, Yuhuangding Hospital of Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Yizi Cong
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Yantai, China
- *Correspondence: Wei Jiang, ; Yizi Cong, ; Hui Yu,
| | - Hui Yu
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- *Correspondence: Wei Jiang, ; Yizi Cong, ; Hui Yu,
| | - Wei Jiang
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China
- *Correspondence: Wei Jiang, ; Yizi Cong, ; Hui Yu,
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Tong T, Gu J, Xu D, Song L, Zhao Q, Cheng F, Yuan Z, Tian S, Yang X, Tian J, Wang K, Jiang T. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis. BMC Med 2022; 20:74. [PMID: 35232446 PMCID: PMC8889703 DOI: 10.1186/s12916-022-02258-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/13/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP. METHODS Patients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed. RESULTS A total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts. CONCLUSIONS The findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.
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Affiliation(s)
- Tong Tong
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jionghui Gu
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Dong Xu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No.1 East Banshan Road, Gongshu District, Hangzhou, 310022, China
| | - Ling Song
- Department of ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiyu Zhao
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Fang Cheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No.1 East Banshan Road, Gongshu District, Hangzhou, 310022, China
| | - Zhiqiang Yuan
- Department of ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shuyuan Tian
- Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Tian'an Jiang
- Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Provincial Key Laboratory of Pulsed Electric Field Technology for Medical Transformation, Hangzhou, 310003, China.
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Rauch GM, Kuerer HM, Jochelson MS. To Look or Not to Look? Yes to Nodal Ultrasound! JOURNAL OF BREAST IMAGING 2021; 3:659-665. [PMID: 38424935 DOI: 10.1093/jbi/wbab079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Indexed: 03/02/2024]
Abstract
Knowledge of axillary nodal status is highly important for correct staging and treatment planning in patients with breast cancer. Axillary US is a recognized highly specific and cost-effective tool for assessing nodal status and guiding appropriate treatment. Axillary US imaging with US-guided biopsy is routinely performed throughout the world. However, because of recent developments in the surgical management of the axilla in patients with newly diagnosed breast cancer (American College of Surgeons Oncology Group [ACOSOG] Z0011 trial) and in patients with breast cancer receiving neoadjuvant systemic therapy (ACOSOG Z1071, SENTinel NeoAdjuvant [SENTINA] and Sentinel Node biopsy aFter NeoAdjuvant Chemotherapy [SN FNAC] trials), some have questioned the utility of axillary US for nodal staging. Here, we review the evidence to date supporting the additional value of axillary US for patients with breast cancer. Nodal US in patients with newly diagnosed breast cancer is useful for staging; in a significant proportion of patients, nodal US identifies additional axillary level II or level III nodal disease, which allows for appropriate treatment of disease. Furthermore, ongoing clinical trials may show that axillary surgery can be omitted in patients with negative findings on axillary US. In patients with lymph node-positive disease undergoing neoadjuvant systemic therapy, nodal US can guide the approach to axillary surgery. A more personalized patient approach, taking into the account tumor biology, among other factors, may help to mitigate the controversy surrounding the role of axillary US in breast cancer patients.
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Affiliation(s)
- Gaiane M Rauch
- The University of Texas MD Anderson Cancer Center, Departments of Abdominal and Breast Imaging, Houston, TX, USA
| | - Henry M Kuerer
- The University of Texas MD Anderson Cancer Center, Department of Breast Surgical Oncology, Houston, TX, USA
| | - Maxine S Jochelson
- Memorial Sloan Kettering Cancer Center, Department of Diagnostic Radiology, New York City, NY, USA
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Tahmasebi A, Qu E, Sevrukov A, Liu JB, Wang S, Lyshchik A, Yu J, Eisenbrey JR. Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence. ULTRASONIC IMAGING 2021; 43:329-336. [PMID: 34416827 DOI: 10.1177/01617346211035315] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared to radiologists. Ultrasound images of 317 axillary lymph nodes from patients referred for ultrasound guided fine needle aspiration or core needle biopsy and corresponding pathology findings were collected. Lymph nodes were classified into benign and malignant groups with histopathological result serving as the reference. Google Cloud AutoML Vision (Mountain View, CA) was used for AI image classification. Three experienced radiologists also classified the images and gave a level of suspicion score (1-5). To test the accuracy of AI, an external testing dataset of 64 images from 64 independent patients was evaluated by three AI models and the three readers. The diagnostic performance of AI and the humans were then quantified using receiver operating characteristics curves. In the complete set of 317 images, AutoML achieved a sensitivity of 77.1%, positive predictive value (PPV) of 77.1%, and an area under the precision recall curve of 0.78, while the three radiologists showed a sensitivity of 87.8% ± 8.5%, specificity of 50.3% ± 16.4%, PPV of 61.1% ± 5.4%, negative predictive value (NPV) of 84.1% ± 6.6%, and accuracy of 67.7% ± 5.7%. In the three external independent test sets, AI and human readers achieved sensitivity of 74.0% ± 0.14% versus 89.9% ± 0.06% (p = .25), specificity of 64.4% ± 0.11% versus 50.1 ± 0.20% (p = .22), PPV of 68.3% ± 0.04% versus 65.4 ± 0.07% (p = .50), NPV of 72.6% ± 0.11% versus 82.1% ± 0.08% (p = .33), and accuracy of 69.5% ± 0.06% versus 70.1% ± 0.07% (p = .90), respectively. These preliminary results indicate AI has comparable performance to trained radiologists and could be used to predict the presence of metastasis in ultrasound images of axillary lymph nodes.
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Affiliation(s)
- Aylin Tahmasebi
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Enze Qu
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Alexander Sevrukov
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Shuo Wang
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Andrej Lyshchik
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua Yu
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - John R Eisenbrey
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
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Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. Eur Radiol 2021; 32:2099-2109. [PMID: 34654965 DOI: 10.1007/s00330-021-08293-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 08/18/2021] [Accepted: 08/21/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage. METHODS In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration. RESULTS In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC. CONCLUSIONS The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients. KEY POINTS • We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
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Grimm LJ. Radiomics: A Primer for Breast Radiologists. JOURNAL OF BREAST IMAGING 2021; 3:276-287. [PMID: 38424774 DOI: 10.1093/jbi/wbab014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Indexed: 03/02/2024]
Abstract
Radiomics has a long-standing history in breast imaging with computer-aided detection (CAD) for screening mammography developed in the late 20th century. Although conventional CAD had widespread adoption, the clinical benefits for experienced breast radiologists were debatable due to high false-positive marks and subsequent increased recall rates. The dramatic growth in recent years of artificial intelligence-based analysis, including machine learning and deep learning, has provided numerous opportunities for improved modern radiomics work in breast imaging. There has been extensive radiomics work in mammography, digital breast tomosynthesis, MRI, ultrasound, PET-CT, and combined multimodality imaging. Specific radiomics outcomes of interest have been diverse, including CAD, prediction of response to neoadjuvant therapy, lesion classification, and survival, among other outcomes. Additionally, the radiogenomics subfield that correlates radiomics features with genetics has been very proliferative, in parallel with the clinical validation of breast cancer molecular subtypes and gene expression assays. Despite the promise of radiomics, there are important challenges related to image normalization, limited large unbiased data sets, and lack of external validation. Much of the radiomics work to date has been exploratory using single-institution retrospective series for analysis, but several promising lines of investigation have made the leap to clinical practice with commercially available products. As a result, breast radiologists will increasingly be incorporating radiomics-based tools into their daily practice in the near future. Therefore, breast radiologists must have a broad understanding of the scope, applications, and limitations of radiomics work.
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Affiliation(s)
- Lars J Grimm
- Duke University, Department of Radiology, Durham, NC, USA
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Chen MY, Gillanders WE. Staging of the Axilla in Breast Cancer and the Evolving Role of Axillary Ultrasound. BREAST CANCER (DOVE MEDICAL PRESS) 2021; 13:311-323. [PMID: 34040436 PMCID: PMC8139849 DOI: 10.2147/bctt.s273039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 04/23/2021] [Indexed: 12/15/2022]
Abstract
Axillary lymph nodes have long been recognized as a route for breast cancer to spread systemically. As a result, staging of the axilla has always played a central role in the treatment of breast cancer. Anatomic staging was believed to be important for two reasons: 1) it predicts prognosis and guides medical therapy, and 2) it is a potential therapy for removal of disease in the axilla. This paradigm has now been called into question. Prognostic information is driven increasingly by tumor biology, and trials such as the ACOSOG Z0011 demonstrates removal of axillary disease is not therapeutic. Staging of the axilla has undergone a dramatic de-escalation; however, sentinel lymph node biopsy (SLNB) is still an invasive surgery and represents a large economic burden on the healthcare system. In this review, we outline the changing paradigms of axillary staging in breast cancer from emphasis on anatomic staging to tumor biology, and the evolving role of axillary ultrasound, bringing patients less invasive and more personalized therapy.
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Affiliation(s)
- Michael Y Chen
- Department of Surgery, Washington University, St Louis, MS, USA
| | - William E Gillanders
- Department of Surgery, Washington University, St Louis, MS, USA.,Siteman Cancer Center in St. Louis, St Louis, MS, USA
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Marostica E, Yu KH. Deep decision support for lymph node metastatic risk evaluation. EBioMedicine 2020; 62:103105. [PMID: 33160208 PMCID: PMC7648189 DOI: 10.1016/j.ebiom.2020.103105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 11/25/2022] Open
Affiliation(s)
- Eliana Marostica
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States; Health Sciences and Technology Program, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States; Department of Pathology, Brigham and Women's Hospital, Boston, MA, United States.
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