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Zheng G, Hou J, Shu Z, Peng J, Han L, Yuan Z, He X, Gong X. Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue. BMC Med Imaging 2024; 24:22. [PMID: 38245712 PMCID: PMC10800060 DOI: 10.1186/s12880-024-01198-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.
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Affiliation(s)
- Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Xiangyang Gong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China.
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Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci 2023; 17:1256351. [PMID: 38027475 PMCID: PMC10665494 DOI: 10.3389/fnins.2023.1256351] [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: 07/10/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.
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Affiliation(s)
- Yanbu Wang
- School of Strength and Conditioning, Beijing Sport University, Beijing, China
| | - Linqing Liu
- Department of Physical Education, Peking University, Beijing, China
| | - Chao Wang
- Institute of Competitive Sports, Beijing Sport University, Beijing, China
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Mohammed AH, Cabrerizo M, Pinzon A, Yaylali I, Jayakar P, Adjouadi M. Graph neural networks in EEG spike detection. Artif Intell Med 2023; 145:102663. [PMID: 37925203 DOI: 10.1016/j.artmed.2023.102663] [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: 10/12/2022] [Revised: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. METHODS Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. RESULTS On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. CONCLUSION Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. SIGNIFICANCE This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.
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Affiliation(s)
- Ahmed Hossam Mohammed
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA.
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
| | - Alberto Pinzon
- Epilepsy Center, Baptist Hospital of Miami, 9090 SW 87th Ct Suite201, Miami, 33176, FL, USA
| | - Ilker Yaylali
- Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, 97239, OR, USA
| | - Prasanna Jayakar
- Brain Institute, Nicklaus Children's Hospital, 3100 SW 62nd Ave, Miami, FL 33155, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
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Xin Z, Yan W, Feng Y, Yunzhi L, Zhang Y, Wang D, Chen W, Peng J, Guo C, Chen Z, Wang X, Zhu J, Lei J. An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study. Cancer Med 2023; 12:19383-19393. [PMID: 37772478 PMCID: PMC10587964 DOI: 10.1002/cam4.6525] [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: 03/30/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND AND PURPOSE Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients. METHODS This study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (SVM), for predicting NACT susceptibility. The model's performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity. RESULTS The SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively. CONCLUSIONS Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.
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Affiliation(s)
- Zhonghong Xin
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Wanying Yan
- Infervision Medical Technology Co., LtdBeijingChina
| | - Yibo Feng
- Infervision Medical Technology Co., LtdBeijingChina
| | - Li Yunzhi
- Department of RadiologyGansu Provincial Maternity and Child‐care HospitalLanzhouChina
| | - Yaping Zhang
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Dawei Wang
- Infervision Medical Technology Co., LtdBeijingChina
| | - Weidao Chen
- Infervision Medical Technology Co., LtdBeijingChina
| | - Jianhong Peng
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Cheng Guo
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Zixian Chen
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Xiaohui Wang
- Department of Gynecology and ObstetricsThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Jun Zhu
- Department of PathologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Junqiang Lei
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
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Han H, Xing L, Chen BT, Liu Y, Zhou TJ, Wang Y, Zhang LF, Li L, Cho CS, Jiang HL. Progress on the pathological tissue microenvironment barrier-modulated nanomedicine. Adv Drug Deliv Rev 2023; 200:115051. [PMID: 37549848 DOI: 10.1016/j.addr.2023.115051] [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: 06/18/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 08/09/2023]
Abstract
Imbalance in the tissue microenvironment is the main obstacle to drug delivery and distribution in the human body. Before penetrating the pathological tissue microenvironment to the target site, therapeutic agents are usually accompanied by three consumption steps: the first step is tissue physical barriers for prevention of their penetration, the second step is inactivation of them by biological molecules, and the third step is a cytoprotective mechanism for preventing them from functioning on specific subcellular organelles. However, recent studies in drug-hindering mainly focus on normal physiological rather than pathological microenvironment, and the repair of damaged physiological barriers is also rarely discussed. Actually, both the modulation of pathological barriers and the repair of damaged physiological barriers are essential in the disease treatment and the homeostasis maintenance. In this review, we present an overview describing the latest advances in the generality of these pathological barriers and barrier-modulated nanomedicine. Overall, this review holds considerable significance for guiding the design of nanomedicine to increase drug efficacy in the future.
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Affiliation(s)
- Han Han
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Lei Xing
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; College of Pharmacy, Yanbian University, Yanji 133002, China
| | - Bi-Te Chen
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Yang Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Tian-Jiao Zhou
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Yi Wang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Ling-Feng Zhang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Ling Li
- Department of Endocrinology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
| | - Chong-Su Cho
- Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Korea.
| | - Hu-Lin Jiang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; College of Pharmacy, Yanbian University, Yanji 133002, China.
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Jiang W, Deng X, Zhu T, Fang J, Li J. ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers. BREAST CANCER (DOVE MEDICAL PRESS) 2023; 15:625-636. [PMID: 37600669 PMCID: PMC10439736 DOI: 10.2147/bctt.s418376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/11/2023] [Indexed: 08/22/2023]
Abstract
Background Neoadjuvant chemotherapy (NAC) plays a significant role in breast cancer (BC) management; however, its efficacy varies among patients. Current evaluation methods may lead to delayed treatment alterations, and traditional imaging modalities often yield inaccurate results. Radiomics, an emerging field in medical imaging, offers potential for improved tumor characterization and personalized medicine. Nevertheless, its application in early and accurately predicting NAC response remains underinvestigated. Objective This study aims to develop an automated breast volume scanner (ABVS)-based radiomics model to facilitate early detection of suboptimal NAC response, ultimately promoting personalized therapeutic approaches for BC patients. Methods This retrospective study involved 248 BC patients receiving NAC. Standard guidelines were followed, and patients were classified as responders or non-responders based on treatment outcomes. ABVS images were obtained before and during NAC, and radiomics features were extracted using the PyRadiomics toolkit. Inter-observer consistency and hierarchical feature selection were assessed. Three machine learning classifiers, logistic regression, support vector machine, and random forest, were trained and validated using a five-fold cross-validation with three repetitions. Model performance was comprehensively evaluated based on discrimination, calibration, and clinical utility. Results Of the 248 BC patients, 157 (63.3%) were responders, and 91 (36.7%) were non-responders. Radiomics feature selection revealed 7 pre-NAC and 6 post-NAC ABVS features, with higher weights for post-NAC features (min >0.05) than pre-NAC (max <0.03). The three post-NAC classifiers demonstrated AUCs of approximately 0.9, indicating excellent discrimination. DCA curves revealed a substantial net benefit when the threshold probability exceeded 40%. Conversely, the three pre-NAC classifiers had AUCs between 0.7 and 0.8, suggesting moderate discrimination and limited clinical utility based on their DCA curves. Conclusion The ABVS-based radiomics model effectively predicted suboptimal NAC responses in BC patients, with early post-NAC classifiers outperforming pre-NAC classifiers in discrimination and clinical utility. It could enhance personalized treatment and improve patient outcomes in BC management.
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Affiliation(s)
- Wei Jiang
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Xiaofei Deng
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Ting Zhu
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Jing Fang
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Jinyao Li
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
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Yuan Y, Yi H, Kang D, Yu J, Guo H, He X, He X. Robust transformed l 1 metric for fluorescence molecular tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107503. [PMID: 37015182 DOI: 10.1016/j.cmpb.2023.107503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/27/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVE Fluorescence molecular tomography (FMT) is a non-invasive molecular imaging modality that can be used to observe the three-dimensional distribution of fluorescent probes in vivo. FMT is a promising imaging technique in clinical and preclinical research that has attracted significant attention. Numerous regularization based reconstruction algorithms have been proposed. However, traditional algorithms that use the squared l2-norm distance usually exaggerate the influence of noise and measurement and calculation errors, and their robustness cannot be guaranteed. METHODS In this study, we propose a novel robust transformed l1 (TL1) metric that interpolates l0 and l1 norms through a nonnegative parameter α∈(0,+∞). The TL1 metric looks like the lp-norm with p∈(0,1). These are markedly different because TL1 metric has two properties, boundedness and Lipschitz-continuity, which make the TL1 criterion suitable distance metric, particularly for robustness, owing to its stronger noise suppression. Subsequently, we apply the proposed metric to FMT and build a robust model to reduce the influence of noise. The nonconvexity of the proposed model made direct optimization difficult, and a continuous optimization method was developed to solve the model. The problem was converted into a difference in convex programming problem for the TL1 metric (DCATL1), and the corresponding algorithm converged linearly. RESULTS Various numerical simulations and in vivo bead-implanted mouse experiments were conducted to verify the performance of the proposed method. The experimental results show that the DCATL1 algorithm is more robust than the state-of-the-art approaches and achieves better source localization and morphology recovery. CONCLUSIONS The in vivo experiments showed that DCATL1 can be used to visualize the distribution of fluorescent probes inside biological tissues and promote preclinical application in small animals, demonstrating the feasibility and effectiveness of the proposed method.
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Affiliation(s)
- Yating Yuan
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Huangjian Yi
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Dizhen Kang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710119, China
| | - Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xiaowei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China.
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Kang C, Sun P, Yang R, Zhang C, Ning W, Liu H. CT radiomics nomogram predicts pathological response after induced chemotherapy and overall survival in patients with advanced laryngeal cancer: A single-center retrospective study. Front Oncol 2023; 13:1094768. [PMID: 37064100 PMCID: PMC10103838 DOI: 10.3389/fonc.2023.1094768] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
PurposeThis study aimed to develop a radiomics nomogram to predict pathological response (PR) after induction chemotherapy (IC) and overall survival (OS) in patients with advanced laryngeal cancer (LC).MethodsThis retrospective study included patients with LC (n = 114) who had undergone contrast computerized tomography (CT); patients were randomly assigned to training (n = 81) and validation cohorts (n = 33). Potential radiomics scores were calculated to establish a model for predicting the PR status using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression analyses were performed to select significant variables for predicting PR status. Kaplan–Meier analysis was performed to assess the risk stratification ability of PR and radiomics score (rad-score) for predicting OS. A prognostic nomogram was developed by integrating radiomics features and clinicopathological characteristics using multivariate Cox regression. All LC patients were stratified as low- and high-risk by the median CT radiomic score, C-index, calibration curve. Additionally, decision curve analysis (DCA) of the nomogram was performed to test model performance and clinical usefulness.ResultsOverall, PR rates were 45.6% (37/81) and 39.3% (13/33) in the training and validation cohorts, respectively. Eight features were optimally selected to build a rad-score model, which was significantly associated with PR and OS. The median OS in the PR group was significantly shorter than that in the non-PR group in both cohorts. Multivariate Cox analysis revealed that volume [hazard ratio, (HR) = 1.43], N stage (HR = 1.46), and rad-score (HR = 2.65) were independent risk factors associated with OS. The above four variables were applied to develop a nomogram for predicting OS, and the DCAs indicated that the predictive performance of the nomogram was better than that of the clinical model.ConclusionFor patients with advanced LC, CT radiomics score was an independent biomarker for estimating PR after IC. Moreover, the nomogram that incorporated radiomics features and clinicopathological factors performed better for individualized OS estimation.
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Affiliation(s)
- Chunmiao Kang
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Pengfeng Sun
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Runqin Yang
- Department of Otolaryngology, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Changming Zhang
- Department of Otolaryngology, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Wenfeng Ning
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Hongsheng Liu
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
- *Correspondence: Hongsheng Liu,
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Alharbi F, Vakanski A. Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering (Basel) 2023; 10:bioengineering10020173. [PMID: 36829667 PMCID: PMC9952758 DOI: 10.3390/bioengineering10020173] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [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: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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Ye J, Hu T, Wu Y, Chen H, Qiu Q, Geng R, Ding H, Zhao X. Near-Infrared Liposome-Capped Au-Rare Earth Bimetallic Nanoclusters for Fluorescence Imaging of Tumor Cells. J Biomed Nanotechnol 2022. [DOI: 10.1166/jbn.2022.3423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Early detection of cancer can effectively improve the survival rate of cancer patients. Fluorescence imaging has the advantages of high sensitivity and rapid imaging, and is widely used in the precise imaging detection of tumors. In this study, five kinds of Au-rare earth bimetallic
nanoclusters (Au/Ln NCs) were prepared by template method using five representative rare earth elements doped with gold. The morphologies, surface charges, sizes, fluorescence quantum yields and maximum fluorescence emission wavelengths of these five kinds of Au/Ln NCs were characterized and
contrasted. The findings indicated that the Au/Ce nanoclusters (Au/Ce NCs) prepared by Ce doping have the longest fluorescence emission wavelength (695 nm) and higher quantum yield, which could effectively avoid the interference of autofluorescence, and was suitable for fluorescence imaging
of tumor cells. In order to improve the specific accumulation of nanoclusters in tumor cells, Au/Ce NCs were coated with folic acid modified liposomes (lip-FA) to constructed a targeted fluorescent imaging probe with near-infrared response (Au/Ce@lip-FA), which was successfully used for fluorescence
imaging of tumor cells. The probe has the characteristics of stable fluorescence signal, good targeting, easy internalization, and safe metabolism, and can provide high-resolution and high-brightness imaging information, which is expected to play an important role in the clinical diagnosis
and surgical treatment of tumors.
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Affiliation(s)
- Jing Ye
- School of Pharmacy, Yancheng Teachers’ University, Yancheng, Jiangsu, 224007, PR China
| | - Tianxiang Hu
- School of Pharmacy, Yancheng Teachers’ University, Yancheng, Jiangsu, 224007, PR China
| | - Yanqi Wu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, 999078, China
| | - Hui Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, 412007, China
| | - Qianqian Qiu
- School of Pharmacy, Yancheng Teachers’ University, Yancheng, Jiangsu, 224007, PR China
| | - Rongqing Geng
- School of Pharmacy, Yancheng Teachers’ University, Yancheng, Jiangsu, 224007, PR China
| | - Hui Ding
- School of Pharmacy, Yancheng Teachers’ University, Yancheng, Jiangsu, 224007, PR China
| | - Xiaojuan Zhao
- School of Pharmacy, Yancheng Teachers’ University, Yancheng, Jiangsu, 224007, PR China
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12
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Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.
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13
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Galati F, Rizzo V, Trimboli RM, Kripa E, Maroncelli R, Pediconi F. MRI as a biomarker for breast cancer diagnosis and prognosis. BJR Open 2022; 4:20220002. [PMID: 36105423 PMCID: PMC9459861 DOI: 10.1259/bjro.20220002] [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: 01/17/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/05/2022] Open
Abstract
Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.
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Affiliation(s)
- Francesca Galati
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Veronica Rizzo
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | | | - Endi Kripa
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Roberto Maroncelli
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
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