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Bardoni C, Spaggiari L, Bertolaccini L. Artificial intelligence in lung cancer. ANNALS OF TRANSLATIONAL MEDICINE 2024; 12:79. [PMID: 39118944 PMCID: PMC11304431 DOI: 10.21037/atm-22-2918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 01/12/2024] [Indexed: 08/10/2024]
Affiliation(s)
- Claudia Bardoni
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
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Gharaibeh NY, De Fazio R, Al-Naami B, Al-Hinnawi AR, Visconti P. Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images. J Imaging 2024; 10:168. [PMID: 39057739 PMCID: PMC11277772 DOI: 10.3390/jimaging10070168] [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: 04/27/2024] [Revised: 06/12/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.
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Affiliation(s)
- Nasr Y. Gharaibeh
- Department of Electrical Engineering, Al-Balqa Applied University, Salt 21163, Jordan;
| | - Roberto De Fazio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
| | - Bassam Al-Naami
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
| | - Abdel-Razzak Al-Hinnawi
- Department of Medical Imaging, Faculty of Allied Medical Sciences, Isra University, Amman 11622, Jordan;
| | - Paolo Visconti
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
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Wu J, Meng H, Zhou L, Wang M, Jin S, Ji H, Liu B, Jin P, Du C. Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study. Sci Rep 2024; 14:15877. [PMID: 38982267 PMCID: PMC11233600 DOI: 10.1038/s41598-024-66751-1] [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: 01/28/2024] [Accepted: 07/03/2024] [Indexed: 07/11/2024] Open
Abstract
Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.
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Affiliation(s)
- Jingran Wu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Hao Meng
- Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Lin Zhou
- Department of Thoracic Surgery, Yuebei People's Hospital Affiliated to Shantou University Medical College, Shaoguan, 512025, China
| | - Meiling Wang
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Shanxiu Jin
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Hongjuan Ji
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China
| | - Bona Liu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China.
| | - Peng Jin
- Department of Oncology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China.
| | - Cheng Du
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang, 110840, China.
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Chen J, Chen A, Yang S, Liu J, Xie C, Jiang H. Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis. Radiother Oncol 2024; 196:110325. [PMID: 38734145 DOI: 10.1016/j.radonc.2024.110325] [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: 11/17/2023] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND AND PURPOSE We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients. MATERIALS AND METHODS We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR. RESULTS We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7. CONCLUSION The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
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Affiliation(s)
- Jinzhan Chen
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Ayun Chen
- Department of Endocrinology, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian 361000, People's Republic of China
| | - Shuwen Yang
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Jiaxin Liu
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Congyi Xie
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.
| | - Hongni Jiang
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.
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Ou DX, Lu CW, Chen LW, Lee WY, Hu HW, Chuang JH, Lin MW, Chen KY, Chiu LY, Chen JS, Chen CM, Hsieh MS. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers (Basel) 2024; 16:2132. [PMID: 38893251 PMCID: PMC11172106 DOI: 10.3390/cancers16112132] [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: 04/19/2024] [Revised: 05/25/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.
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Affiliation(s)
- De-Xiang Ou
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Chao-Wen Lu
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
| | - Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Wen-Yao Lee
- Division of Thoracic Surgery, Department of Surgery, Fu Jen Catholic University Hospital, No. 69, Guizi Road, Taishan District, New Taipei City 24352, Taiwan;
| | - Hsiang-Wei Hu
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
| | - Jen-Hao Chuang
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Mong-Wei Lin
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Kuan-Yu Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Ling-Ying Chiu
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Jin-Shing Chen
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Min-Shu Hsieh
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
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Jiang Z, Gandomkar Z, Trieu PDY, Taba ST, Barron ML, Lewis SJ. AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions. Sci Rep 2024; 14:11893. [PMID: 38789575 PMCID: PMC11126609 DOI: 10.1038/s41598-024-62324-4] [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: 12/11/2023] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions. The Globally-aware Multiple Instance Classifier (GMIC) and Global-Local Activation Maps (GLAM) models were fine-tuned for our dataset. We evaluated the sensitivity of both models on missed cancers retrospectively identified by a panel of three radiologists who reviewed prior examinations of 729 cancer cases detected in a screening program with double reading practice. Two of these experts annotated the lesions, and based on their concordance levels, cases were categorized as 'almost perfect,' 'substantial,' 'moderate,' and 'poor.' We employed Similarity or Histogram Intersection (SIM) and Kullback-Leibler Divergence (KLD) metrics to compare saliency maps of malignant cases from the AI model with annotations from radiologists in each category. In total, 24.82% of cancers were labeled as "missed." The performance of GMIC and GLAM on the missed cancer cases was 82.98% and 79.79%, respectively, while for the true screen-detected cancers, the performances were 89.54% and 87.25%, respectively (p-values for the difference in sensitivity < 0.05). As anticipated, SIM and KLD from saliency maps were best in 'almost perfect,' followed by 'substantial,' 'moderate,' and 'poor.' Both GMIC and GLAM (p-values < 0.05) exhibited greater sensitivity at higher concordance. Even in a screening program with independent double reading, adding AI could potentially identify missed cancers. However, the challenging-to-locate lesions for radiologists impose a similar challenge for AI.
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Affiliation(s)
- Zhengqiang Jiang
- Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
| | - Ziba Gandomkar
- Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Phuong Dung Yun Trieu
- Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Seyedamir Tavakoli Taba
- Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Melissa L Barron
- Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Sarah J Lewis
- Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
- School of Health Sciences, Western Sydney University, Campbelltown, Australia
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Bertolaccini L, Casiraghi M, Uslenghi C, Maiorca S, Spaggiari L. Recent advances in lung cancer research: unravelling the future of treatment. Updates Surg 2024:10.1007/s13304-024-01841-3. [PMID: 38581618 DOI: 10.1007/s13304-024-01841-3] [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: 03/06/2024] [Accepted: 03/24/2024] [Indexed: 04/08/2024]
Abstract
Lung cancer, a multifaceted disease, demands tailored therapeutic approaches due to its diverse subtypes and stages. This comprehensive review explores the intricate landscape of lung cancer research, delving into recent breakthroughs and their implications for diagnosis, therapy, and prevention. Genomic profiling and biomarker identification have ushered in the era of personalised medicine, enabling targeted therapies that minimise harm to healthy tissues while effectively combating cancer cells. The relationship between pulmonary tuberculosis and lung cancer is examined, shedding light on potential mechanisms linking these two conditions. Early detection methods, notably low-dose computed tomography scans, have significantly improved patient outcomes, emphasising the importance of timely interventions. There has been a growing interest in segmentectomy as a surgical intervention for early-stage lung cancer in recent years. Immunotherapy has emerged as a transformative approach, harnessing the body's immune system to recognise and eliminate cancer cells. Combining immunotherapy with traditional treatments, such as chemotherapy and targeted therapies, has shown enhanced efficacy, addressing the disease's heterogeneity and overcoming drug resistance. Precision medicine, guided by genomic profiling, has enabled the development of targeted therapies like tyrosine kinase inhibitors, offering personalised treatments tailored to individual patients. Challenges such as drug resistance and limited accessibility to advanced therapies persist, emphasising the need for collaborative efforts and innovative technologies like artificial intelligence. Despite challenges, ongoing interdisciplinary collaborations and technological advancements offer hope for a future where lung cancer is treatable and preventable, reducing the burden on patients and healthcare systems worldwide.
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Affiliation(s)
- Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy.
| | - Monica Casiraghi
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Clarissa Uslenghi
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Sebastiano Maiorca
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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8
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Winter H, Eichhorn M, Eichhorn F, Grott M. [Modern individualized diagnostics and treatment of non-small cell lung cancer]. CHIRURGIE (HEIDELBERG, GERMANY) 2024; 95:280-287. [PMID: 38376521 DOI: 10.1007/s00104-024-02037-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/21/2024]
Abstract
Approximately one half of patients with non-small cell lung cancer (NSCLC) are diagnosed at resectable tumor stages (I-IIIA), which can potentially be curatively treated. In the early tumor stages (tumor diameter ≤2 cm) sublobar resection (segmentectomy or atypical wedge resection) leads to a 5‑year long-term survival comparable to lobectomy. The use of immunotherapy, especially within the framework of neoadjuvant treatment, is anticipated to change the surgical treatment of NSCLC in the future. With the introduction of lung cancer screening for certain risk groups in Germany planned for 2024, lung tumors can be expected to be diagnosed at earlier stages and more frequently curatively treated. This article provides an overview of the potential impact of lung cancer screening, modern minimally invasive surgical techniques and neoadjuvant treatment concepts for the surgical treatment of NSCLC.
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Affiliation(s)
- Hauke Winter
- Thoraxchirurgie, Thoraxklinik Heidelberg, Roentgenstraße 1, 69126, Heidelberg, Deutschland.
| | - Martin Eichhorn
- Thoraxchirurgie, Thoraxklinik Heidelberg, Roentgenstraße 1, 69126, Heidelberg, Deutschland
| | - Florian Eichhorn
- Thoraxchirurgie, Thoraxklinik Heidelberg, Roentgenstraße 1, 69126, Heidelberg, Deutschland
| | - Matthias Grott
- Thoraxchirurgie, Thoraxklinik Heidelberg, Roentgenstraße 1, 69126, Heidelberg, Deutschland
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Kim PJ, Hwang HS, Choi G, Sung HJ, Ahn B, Uh JS, Yoon S, Kim D, Chun SM, Jang SJ, Go H. A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma. Sci Rep 2024; 14:6366. [PMID: 38493247 PMCID: PMC10944489 DOI: 10.1038/s41598-024-56867-9] [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: 04/18/2023] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer's features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p < 0.001). The HR group was associated with specific histopathological features such as poorly differentiated components, complex glandular pattern components, tumor spread through air spaces, and a higher grade. In the HR group, pleural invasion, necrosis, and lymphatic invasion were more frequent, and the size of the invasion was larger (all, p < 0.001). Several genetic mutations, including TP53 (p = 0.007) mutations, were more frequently found in the HR group. The results of stages I-II were similar to those of the general cohort. DL-based model can predict the recurrence risk of LUAD and identify the presence of the TP53 gene mutation by analyzing histopathologic features.
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Affiliation(s)
- Pil-Jong Kim
- School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Hee Sang Hwang
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Gyuheon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyun-Jung Sung
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Bokyung Ahn
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ji-Su Uh
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Shinkyo Yoon
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Deokhoon Kim
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sung-Min Chun
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Se Jin Jang
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Heounjeong Go
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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10
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Sathish G, Monavarshini LK, Sundaram K, Subramanian S, Kannayiram G. Immunotherapy for lung cancer. Pathol Res Pract 2024; 254:155104. [PMID: 38244436 DOI: 10.1016/j.prp.2024.155104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/22/2024]
Abstract
Immune checkpoint blockers have transformed non-small-cell lung cancer treatment, but they can lead to autoimmune and inflammatory side effects, leading to the concurrent use of immunosuppressive treatments. In this analysis, we delve into the potential of antibodies checkpoint blockade, focusing on CTLA-4 inhibition using ipilimumab, as a groundbreaking cancer immunotherapy. We also concentrate on the role of biomarkers, particularly PD-L1 activity and mutation significance, in predicting the response to programmed cell death protein 1 blockage and the prevalence of side effects associated with immune-related side effects. In describing the patterns of cancer response to immunotherapy, we underline the limitations of response assessment criteria like RECIST and World Health Organization. We also stress the necessity of ongoing studies and clinical trials, standardized guidelines, and additional research to improve response assessment in the era of immunotherapy.
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Affiliation(s)
- Girshani Sathish
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - L K Monavarshini
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Keerthi Sundaram
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Sendilvelan Subramanian
- Deparment of Mechanical Engineering, Dr.MGR Educational and Research Institute, Maduravoyal, Chennai 600095, India
| | - Gomathi Kannayiram
- Department of Biotechnology, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai 600095, India.
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [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: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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Jiang Z, Gandomkar Z, Trieu PD(Y, Tavakoli Taba S, Barron ML, Obeidy P, Lewis SJ. Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration. Cancers (Basel) 2024; 16:322. [PMID: 38254813 PMCID: PMC10814142 DOI: 10.3390/cancers16020322] [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: 12/08/2023] [Revised: 01/07/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.
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Affiliation(s)
- Zhengqiang Jiang
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
| | - Ziba Gandomkar
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
| | - Phuong Dung (Yun) Trieu
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
| | - Seyedamir Tavakoli Taba
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
| | - Melissa L. Barron
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
| | - Peyman Obeidy
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
| | - Sarah J. Lewis
- Discipline of Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; (Z.G.); (P.D.T.); (S.T.T.); (M.L.B.); (P.O.)
- School of Health Sciences, Western Sydney University, Campbelltown 2560, Australia
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Srivastava S, Jayaswal N, Kumar S, Sharma PK, Behl T, Khalid A, Mohan S, Najmi A, Zoghebi K, Alhazmi HA. Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management. Cell Signal 2024; 113:110932. [PMID: 37866667 DOI: 10.1016/j.cellsig.2023.110932] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/15/2023] [Accepted: 10/18/2023] [Indexed: 10/24/2023]
Abstract
Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.
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Affiliation(s)
- Shriyansh Srivastava
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), Sector 3 Pushp Vihar, New Delhi 110017, India; Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida 203201, India
| | - Nandani Jayaswal
- Accurate College of Pharmacy, 49, Knowledge Park-III, Greater Noida, UP, India
| | - Sachin Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), Sector 3 Pushp Vihar, New Delhi 110017, India
| | - Pramod Kumar Sharma
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida 203201, India
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Sahibzada Ajit Singh Nagar, Punjab, India.
| | - Asaad Khalid
- Substance Abuse and Toxicology Research Centre, Jazan University, Jazan 45142, Saudi Arabia; Medicinal and Aromatic Plants Research Institute, National Center for Research, P.O. Box: 2424, Khartoum 11111, Sudan
| | - Syam Mohan
- Substance Abuse and Toxicology Research Centre, Jazan University, Jazan 45142, Saudi Arabia; Center for Global Health Research, Saveetha Medical College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India; School of Health Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.
| | - Asim Najmi
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, P.O. Box 114, Jazan, Saudi Arabia
| | - Khalid Zoghebi
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, P.O. Box 114, Jazan, Saudi Arabia
| | - Hassan A Alhazmi
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, P.O. Box 114, Jazan, Saudi Arabia
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Lin CY, Guo SM, Lien JJJ, Lin WT, Liu YS, Lai CH, Hsu IL, Chang CC, Tseng YL. Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT. LA RADIOLOGIA MEDICA 2024; 129:56-69. [PMID: 37971691 PMCID: PMC10808169 DOI: 10.1007/s11547-023-01730-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 09/21/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. MATERIALS AND METHODS The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task. RESULTS The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%. CONCLUSION Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.
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Affiliation(s)
- Chia-Ying Lin
- Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Shu-Mei Guo
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Jenn-Jier James Lien
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Wen-Tsen Lin
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Yi-Sheng Liu
- Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Chao-Han Lai
- Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - I-Lin Hsu
- Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C
| | - Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan, R.O.C..
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.1, University Road, Tainan City, 701, Taiwan, R.O.C
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Stoichita A, Ghita M, Mahler B, Vlasceanu S, Ghinet A, Mosteanu M, Cioacata A, Udrea A, Marcu A, Mitra GD, Ionescu CM, Iliesiu A. Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. J Clin Med 2023; 12:7115. [PMID: 38002725 PMCID: PMC10672398 DOI: 10.3390/jcm12227115] [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: 09/19/2023] [Revised: 10/27/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND In December 2019 the World Health Organization announced that the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection had become a global pandemic. The most affected organ by the novel virus is the lung, and imaging exploration of the thorax using computer tomography (CT) scanning and X-ray has had an important impact. MATERIALS AND METHODS We assessed the prevalence of lung lesions in vaccinated versus unvaccinated SARS-CoV-2 patients using an artificial intelligence (AI) platform provided by Medicai. The software analyzes the CT scans, performing the lung and lesion segmentation using a variant of the U-net convolutional network. RESULTS We conducted a cohort study at a tertiary lung hospital in which we included 186 patients: 107 (57.52%) male and 59 (42.47%) females, of which 157 (84.40%) were not vaccinated for SARS-CoV-2. Over five times more unvaccinated patients than vaccinated ones are admitted to the hospital and require imaging investigations. More than twice as many unvaccinated patients have more than 75% of the lungs affected. Patients in the age group 30-39 have had the most lung lesions at almost 69% of both lungs affected. Compared to vaccinated patients with comorbidities, unvaccinated patients with comorbidities had developed increased lung lesions by 5%. CONCLUSION The study revealed a higher percentage of lung lesions among unvaccinated SARS-CoV-2-positive patients admitted to The National Institute of Pulmonology "Marius Nasta" in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation.
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Affiliation(s)
- Alexandru Stoichita
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Maria Ghita
- Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium; (M.G.); (C.M.I.)
- Faculty of Medicine and Health Sciences, Antwerp University, 2610 Wilrijk, Belgium
| | - Beatrice Mahler
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Silviu Vlasceanu
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Andreea Ghinet
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Madalina Mosteanu
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
- Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Andreea Cioacata
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Andreea Udrea
- Medicai, 020961 Bucharest, Romania; (A.U.); (A.M.); (G.D.M.)
| | - Alina Marcu
- Medicai, 020961 Bucharest, Romania; (A.U.); (A.M.); (G.D.M.)
| | | | - Clara Mihaela Ionescu
- Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium; (M.G.); (C.M.I.)
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Adriana Iliesiu
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- Clinical Hospital “Prof. Dr. Th. Burghele”, 061344 Bucharest, Romania
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Gandhi Z, Gurram P, Amgai B, Lekkala SP, Lokhandwala A, Manne S, Mohammed A, Koshiya H, Dewaswala N, Desai R, Bhopalwala H, Ganti S, Surani S. Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes. Cancers (Basel) 2023; 15:5236. [PMID: 37958411 PMCID: PMC10650618 DOI: 10.3390/cancers15215236] [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: 09/26/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
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Affiliation(s)
- Zainab Gandhi
- Department of Internal Medicine, Geisinger Wyoming Valley Medical Center, Wilkes Barre, PA 18711, USA
| | - Priyatham Gurram
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Birendra Amgai
- Department of Internal Medicine, Geisinger Community Medical Center, Scranton, PA 18510, USA;
| | - Sai Prasanna Lekkala
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Alifya Lokhandwala
- Department of Medicine, Jawaharlal Nehru Medical College, Wardha 442001, India;
| | - Suvidha Manne
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Adil Mohammed
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48602, USA;
| | - Hiren Koshiya
- Department of Internal Medicine, Prime West Consortium, Inglewood, CA 92395, USA;
| | - Nakeya Dewaswala
- Department of Cardiology, University of Kentucky, Lexington, KY 40536, USA;
| | - Rupak Desai
- Independent Researcher, Atlanta, GA 30079, USA;
| | - Huzaifa Bhopalwala
- Department of Internal Medicine, Appalachian Regional Hospital, Hazard, KY 41701, USA; (H.B.); (S.G.)
| | - Shyam Ganti
- Department of Internal Medicine, Appalachian Regional Hospital, Hazard, KY 41701, USA; (H.B.); (S.G.)
| | - Salim Surani
- Departmet of Pulmonary, Critical Care Medicine, Texas A&M University, College Station, TX 77845, USA;
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da Silva HEC, Santos GNM, Leite AF, Mesquita CRM, Figueiredo PTDS, Stefani CM, de Melo NS. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews. PLoS One 2023; 18:e0292063. [PMID: 37796946 PMCID: PMC10553229 DOI: 10.1371/journal.pone.0292063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 09/12/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND AND PURPOSE In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients. DATA SOURCES The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately. RESULTS In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis. CONCLUSIONS The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems. TRIAL REGISTRATION Systematic review registration. Prospero registration number: CRD42022307403.
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Affiliation(s)
| | | | - André Ferreira Leite
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
| | | | | | - Cristine Miron Stefani
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
| | - Nilce Santos de Melo
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
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Zhuan B, Ma HH, Zhang BC, Li P, Wang X, Yuan Q, Yang Z, Xie J. Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study. Front Oncol 2023; 13:1158948. [PMID: 37576878 PMCID: PMC10419203 DOI: 10.3389/fonc.2023.1158948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/03/2023] [Indexed: 08/15/2023] Open
Abstract
Background Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient's disease and routine clinical data. Methods Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People's Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models. Results 135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987. Conclusion The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.
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Affiliation(s)
- Bing Zhuan
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, China
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, China
| | - Hong-Hong Ma
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, China
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, China
| | - Bo-Chao Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Ping Li
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, China
- Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, China
| | - Xi Wang
- Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Qun Yuan
- Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Zhao Yang
- Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
| | - Jun Xie
- Department of Thoracic Surgery, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China
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Hochhegger B, Pasini R, Roncally Carvalho A, Rodrigues R, Altmayer S, Kayat Bittencourt L, Marchiori E, Forghani R. Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications. Semin Roentgenol 2023; 58:184-195. [PMID: 37087139 DOI: 10.1053/j.ro.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.
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21
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Bidzińska J, Szurowska E. See Lung Cancer with an AI. Cancers (Basel) 2023; 15:cancers15041321. [PMID: 36831662 PMCID: PMC9954317 DOI: 10.3390/cancers15041321] [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: 01/20/2023] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
A lot has happened in the field of lung cancer screening in recent months. The ongoing discussion and documentation published by the scientific community and policymakers are of great importance to the entire European community and perhaps beyond. Lung cancer is the main worldwide killer. Low-dose computed tomography-based screening, together with smoking cessation, is the only tool to fight lung cancer, as it has already been proven in the United States of America but also European randomized controlled trials. Screening requires a lot of well-organized specialized work, but it can be supported by artificial intelligence (AI). Here we discuss whether and how to use AI for patients, radiologists, pulmonologists, thoracic surgeons, and all hospital staff supporting screening process benefits.
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22
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Candita G, Rossi S, Cwiklinska K, Fanni SC, Cioni D, Lencioni R, Neri E. Imaging Diagnosis of Hepatocellular Carcinoma: A State-of-the-Art Review. Diagnostics (Basel) 2023; 13:diagnostics13040625. [PMID: 36832113 PMCID: PMC9955560 DOI: 10.3390/diagnostics13040625] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
Hepatocellular carcinoma (HCC) remains not only a cause of a considerable part of oncologic mortality, but also a diagnostic and therapeutic challenge for healthcare systems worldwide. Early detection of the disease and consequential adequate therapy are imperative to increase patients' quality of life and survival. Imaging plays, therefore, a crucial role in the surveillance of patients at risk, the detection and diagnosis of HCC nodules, as well as in the follow-up post-treatment. The unique imaging characteristics of HCC lesions, deriving mainly from the assessment of their vascularity on contrast-enhanced computed tomography (CT), magnetic resonance (MR) or contrast-enhanced ultrasound (CEUS), allow for a more accurate, noninvasive diagnosis and staging. The role of imaging in the management of HCC has further expanded beyond the plain confirmation of a suspected diagnosis due to the introduction of ultrasound and hepatobiliary MRI contrast agents, which allow for the detection of hepatocarcinogenesis even at an early stage. Moreover, the recent technological advancements in artificial intelligence (AI) in radiology contribute an important tool for the diagnostic prediction, prognosis and evaluation of treatment response in the clinical course of the disease. This review presents current imaging modalities and their central role in the management of patients at risk and with HCC.
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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24
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Thong LT, Chou HS, Chew HSJ, Lau Y. Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis. Lung Cancer 2023; 176:4-13. [PMID: 36566582 DOI: 10.1016/j.lungcan.2022.12.002] [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: 04/10/2022] [Revised: 12/04/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening. OBJECTIVE To systematically review the DTA of AI-based imaging for lung cancer screening. METHODS PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence. RESULTS Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI: 91.4 % to 96.7 %) and specificity was 93.6 % (95 % CI: 88.5 % to 96.6 %). Subgroup analyses revealed that similar results were found among different types of AI, region, data source, and year of publication, but the overall quality of evidence was very low. CONCLUSION AI-based imaging could effectively detect lung cancer and be incorporated into lung cancer screening programs. Further high-quality DTA studies on large lung cancer screening populations are required to validate AI's role in early lung cancer detection.
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Affiliation(s)
- Lay Teng Thong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Hui Shan Chou
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Hussain Ali Y, Chinnaperumal S, Marappan R, Raju SK, Sadiq AT, Farhan AK, Srinivasan P. Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things. Bioengineering (Basel) 2023; 10:bioengineering10020138. [PMID: 36829633 PMCID: PMC9952033 DOI: 10.3390/bioengineering10020138] [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: 12/23/2022] [Revised: 01/07/2023] [Accepted: 01/11/2023] [Indexed: 01/22/2023] Open
Abstract
The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques.
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Affiliation(s)
- Yossra Hussain Ali
- Department of Computer Sciences, University of Technology, Baghdad 10066, Iraq
| | - Seelammal Chinnaperumal
- Department of Computer Science and Engineering, Solamalai College of Engineering, Madurai 625020, India
| | - Raja Marappan
- School of Computing, Sastra Deemed University, Thanjavur 613401, India
| | - Sekar Kidambi Raju
- School of Computing, Sastra Deemed University, Thanjavur 613401, India
- Correspondence:
| | - Ahmed T. Sadiq
- Department of Computer Sciences, University of Technology, Baghdad 10066, Iraq
| | - Alaa K. Farhan
- Department of Computer Sciences, University of Technology, Baghdad 10066, Iraq
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HUANG CONGGAI, LUO XING, WANG SHAOHUA, WAN YU, WANG JIEQIONG, TANG XIAOQIN, SCHATZ CHRISTOPH, ZHANG HUILING, HAYBAECK JOHANNES, YANG ZHIHUI. Minimally Invasive Cytopathology and Accurate Diagnosis: Technical Procedures and Ancillary Techniques. In Vivo 2023; 37:11-21. [PMID: 36593030 PMCID: PMC9843757 DOI: 10.21873/invivo.13050] [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: 11/08/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 01/03/2023]
Abstract
In recent years, the demand for cytopathological accurate diagnoses has increased as expanding minimally invasive procedures obtain materials from patients with advanced cancer for diagnostic, prognostic, and predictive purposes. However, inadequate knowledge of cytopathological technical procedures and ancillary techniques by clinicians remains the most common reason for the limited availability of cytopathology. The objectives of this review were to understand the technical procedures, ancillary techniques, and application and effectiveness of various types of tests in cytopathology. Each of the many ancillary technologies described in the literature has specific advantages and limitations and laboratories select one or more methods depending on their infrastructure and expertise to achieve the goal from initial screening of the disease to the final diagnosis of the cytopathology. This paper systematically reviews the development of cytopathology, summarizes the existing problems in cytopathology and the new progress of auxiliary examination, to provide a theoretical basis for the advanced development of cytopathological diagnostic technologies and to consolidate the minimally invasive and accurate diagnosis of cytopathologies for clinicians. Cytopathology offers many advantages over other clinical examinations, particularly for minimally invasive and accurate diagnosis.
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Affiliation(s)
- CONGGAI HUANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - XING LUO
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - SHAOHUA WANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - YU WAN
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - JIEQIONG WANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - XIAOQIN TANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - CHRISTOPH SCHATZ
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - HUILING ZHANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - JOHANNES HAYBAECK
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria,Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - ZHIHUI YANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
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27
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Sethy PK, Geetha Devi A, Padhan B, Behera SK, Sreedhar S, Das K. Lung cancer histopathological image classification using wavelets and AlexNet. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:211-221. [PMID: 36463485 DOI: 10.3233/xst-221301] [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] [Indexed: 06/17/2023]
Abstract
Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.
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Affiliation(s)
| | - A Geetha Devi
- Department of Electronics and Communication Engineering, PVP Siddhartha Institute of Technology, Vijayawada, AP, India
| | - Bikash Padhan
- Department of Electronics, Sambalpur University, Jyoti Vihar, Burla, India
| | | | | | - Kalyan Das
- Department Computer Science Engineering and Application, Sambalpur University Institute of Information Technology, Burla, India
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28
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Selvan P, Thavarajah R, Ranganathan K. NLP and oral health information. J Oral Maxillofac Pathol 2023; 27:15-17. [PMID: 37234303 PMCID: PMC10207217 DOI: 10.4103/jomfp.jomfp_85_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 05/27/2023] Open
Affiliation(s)
- Preetha Selvan
- Department of Oral and Maxillofacial Pathology, Ragas Dental College and Hospital, Uthandi, Chennai, Tamil Nadu, India
| | - Rooban Thavarajah
- Department of Oral and Maxillofacial Pathology, Ragas Dental College and Hospital, Uthandi, Chennai, Tamil Nadu, India
| | - Kannan Ranganathan
- Department of Oral and Maxillofacial Pathology, Ragas Dental College and Hospital, Uthandi, Chennai, Tamil Nadu, India
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29
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Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics (Basel) 2022; 12:diagnostics12112644. [PMID: 36359485 PMCID: PMC9689810 DOI: 10.3390/diagnostics12112644] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/26/2022] [Accepted: 10/29/2022] [Indexed: 11/30/2022] Open
Abstract
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.
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30
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Abbasian MH, Ardekani AM, Sobhani N, Roudi R. The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment. Cancers (Basel) 2022; 14:5144. [PMID: 36291929 PMCID: PMC9600051 DOI: 10.3390/cancers14205144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 08/17/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.
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Affiliation(s)
- Mohammad Hadi Abbasian
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Ali M. Ardekani
- Department of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Navid Sobhani
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Raheleh Roudi
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA 94305, USA
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