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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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2
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Wojtera B, Szewczyk M, Pieńkowski P, Golusiński W. Artificial intelligence in head and neck surgery: Potential applications and future perspectives. J Surg Oncol 2024; 129:1051-1055. [PMID: 38419212 DOI: 10.1002/jso.27616] [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: 02/01/2024] [Revised: 02/05/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.
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Affiliation(s)
- Bartosz Wojtera
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Mateusz Szewczyk
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Piotr Pieńkowski
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Wojciech Golusiński
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
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3
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Kang CJ, Lee LY, Ng SH, Lin CY, Fan KH, Chen WC, Lin JC, Tsai YT, Lee SR, Chien CY, Hua CH, Ping Wang C, Chen TM, Terng SD, Tsai CY, Wang HM, Hsieh CH, Yeh CH, Lin CH, Tsao CK, Cheng NM, Fang TJ, Huang SF, Lee LA, Fang KH, Wang YC, Lin WN, Hsin LJ, Yen TC, Wen YW, Liao CT. Should sub-millimeter margins be deemed positive in oral cavity squamous cell carcinoma? Oral Oncol 2024; 151:106745. [PMID: 38460286 DOI: 10.1016/j.oraloncology.2024.106745] [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: 01/10/2024] [Revised: 02/20/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND While several studies have indicated that a margin status of < 1 mm should be classified as a positive margin in oral cavity squamous cell carcinoma (OCSCC), there is a lack of extensive cohort studies comparing the clinical outcomes between patients with positive margins and margins < 1 mm. METHODS Between 2011 and 2020, we identified 18,416 Taiwanese OCSCC patients who underwent tumor resection and neck dissection. Of these, 311 had margins < 1 mm and 1013 had positive margins. To compare patients with margins < 1 mm and those with positive margins, a propensity score (PS)-matched analysis (n = 253 in each group) was conducted. RESULTS The group with margins < 1 mm displayed a notably higher prevalence of several variables: 1) tongue subsite, 2) younger age, 3) smaller depth of invasion), 4) early tumor stage, and 5) treatment with surgery alone. Patients with margins < 1 mm demonstrated significantly better disease-specific survival (DSS) and overall survival (OS) rates compared to those with positive margins (74 % versus 53 %, 65 % versus 43 %, both p < 0.0001). Multivariable analysis further confirmed that positive margins were an independent predictor of worse 5-year DSS (hazard ratio [HR] = 1.38, p = 0.0103) and OS (HR = 1.28, p = 0.0222). In the PS-matched cohort, the 5-year outcomes for patients with margins < 1 mm compared to positive margins were as follows: DSS, 71 % versus 59 %, respectively (p = 0.0127) and OS, 60 % versus 48 %, respectively (p = 0.0398). CONCLUSIONS OCSCC patients with a margin status < 1 mm exhibited distinct clinicopathological characteristics and a more favorable prognosis compared to those with positive resection margins.
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Affiliation(s)
- Chung-Jan Kang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Li-Yu Lee
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Shu-Hang Ng
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Chien-Yu Lin
- Department of Radiation Oncology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Kang-Hsing Fan
- Department of Radiation Oncology, New Taipei Municipal TuCheng Hospital, Taiwan, ROC
| | - Wen-Cheng Chen
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Chiayi, Taiwan, ROC
| | - Jin-Ching Lin
- Department of Radiation Oncology, Changhua Christian Hospital, Changhua, Taiwan, ROC
| | - Yao-Te Tsai
- Department of Otorhinolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Chiayi, Taiwan, ROC
| | - Shu-Ru Lee
- Research Service Center for Health Information, Chang Gung University, Taoyuan Taiwan, ROC
| | - Chih-Yen Chien
- Department of Otolaryngology, Chang Gung Memorial Hospital Kaohsiung Medical Center, Chang Gung University College of Medicine, Taiwan, ROC
| | - Chun-Hung Hua
- Department of Otorhinolaryngology, China Medical University Hospital, Taichung, Taiwan, ROC
| | - Cheng Ping Wang
- Department of Otolaryngology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan, ROC
| | - Tsung-Ming Chen
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, ROC
| | - Shyuang-Der Terng
- Department of Head and Neck Surgery, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan, ROC
| | - Chi-Ying Tsai
- Department of Oral and Maxillofacial Surgery, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Hung-Ming Wang
- Department of Medical Oncology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Chia-Hsun Hsieh
- Department of Medical Oncology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Chih-Hua Yeh
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Chih-Hung Lin
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Chung-Kan Tsao
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Nai-Ming Cheng
- Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Tuan-Jen Fang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Shiang-Fu Huang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Li-Ang Lee
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Ku-Hao Fang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Yu-Chien Wang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Wan-Ni Lin
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Li-Jen Hsin
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Tzu-Chen Yen
- Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC
| | - Yu-Wen Wen
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC; Division of Thoracic Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan, ROC.
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan, ROC.
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Wang W, Ruan S, Xie Y, Fang S, Yang J, Li X, Zhang Y. Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer. Clin Exp Otorhinolaryngol 2024; 17:85-97. [PMID: 38246983 PMCID: PMC10933807 DOI: 10.21053/ceo.2023.00026] [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: 10/16/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVES The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. METHODS Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin-stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm. RESULTS Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model's high clinical applicability. CONCLUSION The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.
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Affiliation(s)
- Weihua Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Suyu Ruan
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yuhang Xie
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shengjian Fang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Junxian Yang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xueyan Li
- Department of Nursing, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu Zhang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
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Badawy M, Balaha HM, Maklad AS, Almars AM, Elhosseini MA. Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs. Biomimetics (Basel) 2023; 8:499. [PMID: 37887629 PMCID: PMC10604828 DOI: 10.3390/biomimetics8060499] [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: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.
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Affiliation(s)
- Mahmoud Badawy
- Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah 41461, Saudi Arabia
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
| | - Hossam Magdy Balaha
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40208, USA
| | - Ahmed S. Maklad
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suif 62521, Egypt
| | - Abdulqader M. Almars
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
| | - Mostafa A. Elhosseini
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
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6
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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Schmitd LB, Perez‐Pacheco C, D'Silva NJ. Nerve density in cancer: Less is better. FASEB Bioadv 2021; 3:773-786. [PMID: 34632313 PMCID: PMC8493966 DOI: 10.1096/fba.2021-00046] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/14/2021] [Accepted: 06/16/2021] [Indexed: 12/12/2022] Open
Abstract
The density of nerves in cancer is emerging as a relevant clinical parameter for patient survival. Nerves in the tumor microenvironment have been associated with poor survival and recurrence, particularly if involved in perineural invasion. However, usually only a few nerves inside a tumor are affected by perineural invasion, while most nerves are not. Mechanistic studies have shown nerve-secreted factors promote tumor growth and invasion thereby making tumors more aggressive. Therefore, the overall number of nerves in the tumor microenvironment should be more representative of the nerve-tumor biological interaction than perineural invasion. This review summarizes the available clinical information about nerve density as a measure of clinical outcome in cancer and explores the mechanisms underlying nerve density in cancer, specifically, neurogenesis, axonogenesis, and neurotropism.
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Affiliation(s)
- Ligia B. Schmitd
- Department of Periodontics and Oral MedicineUniversity of Michigan School of DentistryAnn ArborMIUSA
| | - Cindy Perez‐Pacheco
- Department of Periodontics and Oral MedicineUniversity of Michigan School of DentistryAnn ArborMIUSA
| | - Nisha J. D'Silva
- Department of Periodontics and Oral MedicineUniversity of Michigan School of DentistryAnn ArborMIUSA
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMIUSA
- Rogel Cancer CenterUniversity of MichiganAnn ArborMIUSA
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9
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Sun R, Lerousseau M, Henry T, Carré A, Leroy A, Estienne T, Niyoteka S, Bockel S, Rouyar A, Alvarez Andres É, Benzazon N, Battistella E, Classe M, Robert C, Scoazec JY, Deutsch É. [Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]. Cancer Radiother 2021; 25:630-637. [PMID: 34284970 DOI: 10.1016/j.canrad.2021.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/19/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
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Affiliation(s)
- R Sun
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France.
| | - M Lerousseau
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - T Henry
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de médecine nucléaire, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - A Carré
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - A Leroy
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - T Estienne
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Niyoteka
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Bockel
- Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - A Rouyar
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - É Alvarez Andres
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - N Benzazon
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - E Battistella
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | | | - C Robert
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - J Y Scoazec
- Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France; Département de biologie et pathologie médicales, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
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- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
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