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Wang N, Xue T, Zheng W, Shao Z, Liu Z, Dai F, Xie Q, Sang J, Ye X. Safety and efficacy of percutaneous biopsy and microwave ablation in patients with pulmonary nodules on antithrombotic therapy: A study with rivaroxaban bridging. Thorac Cancer 2024. [PMID: 39155057 DOI: 10.1111/1759-7714.15425] [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: 07/04/2024] [Revised: 07/27/2024] [Accepted: 07/30/2024] [Indexed: 08/20/2024] Open
Abstract
BACKGROUND To evaluate the safety and efficacy of percutaneous biopsy and microwave ablation (B + MWA) in patients with pulmonary nodules (PNs) who are receiving antithrombotic therapy by rivaroxaban as bridging therapy. METHODS The study comprised 187 patients with PNs who underwent 187 B + MWA sessions from January 1, 2020, to December 31, 2021. The enrolled patients were divided into two groups: Group A, who received antithrombotic therapy five days before the procedure and received rivaroxaban as a bridging drug during hospitalization, and group B, who had no antithrombotic treatment. Information about the technical success rate, positive biopsy rate, complete ablative rate, and major complications were collected and analyzed. RESULTS Group A comprised 53 patients and group B comprised 134 patients. The technical success rate was 100% in both groups. The positive biopsy rates were 88.68% and 91.04%, respectively (p = 0.6211, X2 = 0.2443). In groups A and B, the complete ablative rates at 6, 12, and 24 months were 100.0% versus 99.25%, 96.23% versus 96.27%, and 88.68% versus 89.55%, respectively. There were no significant differences in bleeding and thrombotic complications between the two groups. No grade 5 complications occurred. CONCLUSIONS It is generally considered safe and effective that patients who are on antithrombotic therapy by rivaroxaban as bridging to undergo B + MWA for treating PNs.
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Affiliation(s)
- Nan Wang
- Department of Oncology, Shandong Provincial Qianfoshan Hospital, Shandong University of Traditional Chinese Medicine, Jinan, People's Republic of China
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan, People's Republic of China
| | - Tianyu Xue
- Department of Clinical Medicine, Jining Medical University, Jining, People's Republic of China
| | - Wenwen Zheng
- Department of Technical, Beijing Rheinhealthcare Technology Co., Limited liability company, Beijing, People's Republic of China
| | - Zhongying Shao
- Department of Hepatology, Tai'an Hospital of Traditional Chinese Medicine, Tai'an, People's Republic of China
| | - Zhuang Liu
- Department of Technical, Beijing Rheinhealthcare Technology Co., Limited liability company, Beijing, People's Republic of China
| | - Faliang Dai
- Department of Minimally invasive, Caoxian People's Hospital, Heze, People's Republic of China
| | - Qi Xie
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan, People's Republic of China
| | - Jing Sang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan, People's Republic of China
| | - Xin Ye
- Department of Oncology, Shandong Provincial Qianfoshan Hospital, Shandong University of Traditional Chinese Medicine, Jinan, People's Republic of China
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Jinan, People's Republic of China
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Dahiya R, Sutariya VB, Gupta SV, Pant K, Ali H, Alhadrawi M, Kaur K, Sharma A, Rajput P, Gupta G, Almujri SS, Chinni SV. Harnessing pyroptosis for lung cancer therapy: The impact of NLRP3 inflammasome activation. Pathol Res Pract 2024; 260:155444. [PMID: 38986361 DOI: 10.1016/j.prp.2024.155444] [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: 05/22/2024] [Revised: 06/22/2024] [Accepted: 06/28/2024] [Indexed: 07/12/2024]
Abstract
Lung cancer is still a global health challenge in terms of high incidence, morbidity, and mortality. Recent scientific studies have determined that pyroptosis, a highly inflammatory form of programmed cell death, can be identified as a potential lung cancer therapeutic target. The NLRP3 inflammasome acts as a critical mediator in this process and, upon activation, activates multiprotein complex formation as well as caspase-1 activation. This process, triggered by a release of pro-inflammatory cytokines, results in pyroptotic cell death. Also, the relationship between the NLRP3 inflammasome and lung cancer was justified by its influence on tumour growth or metastasis. The molecular pathways produce progenitive mediators and remake the tissue. Finally, targeting NLRP3 inflammasome for pyroptosis induction and inhibition of its activation appears to be a promising lung cancer treatment approach. This technique makes cancer treatment more promising and personalized. This review explores the role of NLRP3 inflammasome activation and its possibilities in lung cancer treatment.
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Affiliation(s)
- Rajiv Dahiya
- School of Pharmacy, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad & Tobago, West Indies
| | - Vijaykumar B Sutariya
- USF Health Taneja College of Pharmacy, University of South Florida, Tampa, FL 33612, USA
| | - Sheeba Varghese Gupta
- USF Health Taneja College of Pharmacy, University of South Florida, Tampa, FL 33612, USA
| | - Kumud Pant
- Graphic Era (Deemed to be University) Clement Town Dehradun, 248002, India; Graphic Era Hill University Clement Town Dehradun, 248002, India.
| | - Haider Ali
- Centre for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India; Department of Pharmacology, Kyrgyz State Medical College, Bishkek, Kyrgyzstan
| | - Merwa Alhadrawi
- College of Technical Engineering, The Islamic University, Najaf, Iraq; College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
| | - Kiranjeet Kaur
- Chandigarh Pharmacy College, Chandigarh Group of Colleges, Jhanjeri, Mohali, Punjab 140307, India
| | - Abhishek Sharma
- Department of Medicine, National Institute of Medical Sciences, NIMS University Rajasthan, Jaipur, India
| | - Pranchal Rajput
- School of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, India
| | - Gaurav Gupta
- Centre for Research Impact & Outcome-Chitkara College of Pharmacy, Chitkara University, Punjab
| | - Salem Salman Almujri
- Department of Pharmacology, College of Pharmacy, King Khalid University, Abha, Asir 61421, Saudi Arabia
| | - Suresh V Chinni
- Department of Biochemistry, Faculty of Medicine, Bioscience, and Nursing, MAHSA University, Jenjarom, Selangor 42610, Malaysia
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Wulaningsih W, Villamaria C, Akram A, Benemile J, Croce F, Watkins J. Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis. Lung 2024:10.1007/s00408-024-00706-1. [PMID: 38782779 DOI: 10.1007/s00408-024-00706-1] [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: 01/15/2024] [Accepted: 05/12/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules. METHODS An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used. RESULTS Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively. CONCLUSION DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.
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Affiliation(s)
- Wahyu Wulaningsih
- The Royal Marsden, London, UK.
- Faculty of Life Sciences & Medicine, King's College London, London, UK.
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Wang Z, Xue F, Sui X, Han W, Song W, Jiang J. Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study. Pulmonology 2024:S2531-0437(24)00040-0. [PMID: 38614860 DOI: 10.1016/j.pulmoe.2024.02.010] [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: 07/23/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography. METHODS We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year). RESULTS In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules. CONCLUSIONS The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.
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Affiliation(s)
- Z Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China; Peking University People's Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases. No. 11 Xizhimen South Street, Beijing, China
| | - F Xue
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China
| | - X Sui
- Department of Radiology, Peking Union Medical College Hospital. No.1 Shuaifuyuan Street, Dongcheng District, Beijing, China
| | - W Han
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China
| | - W Song
- Department of Radiology, Peking Union Medical College Hospital. No.1 Shuaifuyuan Street, Dongcheng District, Beijing, China
| | - J Jiang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China.
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Allehebi A, Al-Omair A, Mahboub B, Koegelenberg CF, Mokhtar M, Madkour AM, Al-Asad K, Selek U, Al-Shamsi HO. Recommended approaches for screening and early detection of lung cancer in the Middle East and Africa (MEA) region: a consensus statement. J Thorac Dis 2024; 16:2142-2158. [PMID: 38617789 PMCID: PMC11009596 DOI: 10.21037/jtd-23-1568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/19/2024] [Indexed: 04/16/2024]
Abstract
Background The prevalence of lung cancer in the Middle East and Africa (MEA) region has steadily increased in recent years and is generally associated with a poor prognosis due to the late detection of most of the cases. We explored the factors leading to delayed diagnoses, as well as the challenges and gaps in the early screening, detection, and referral framework for lung cancer in the MEA. Methods A steering committee meeting was convened in October 2022, attended by a panel of ten key external experts in the field of oncology from the Kingdom of Saudi Arabia, United Arab Emirates, South Africa, Egypt, Lebanon, Jordan, and Turkey, who critically and extensively analyzed the current unmet needs and challenges in the screening and early diagnosis of lung cancer in the region. Results As per the experts' opinion, lack of awareness about disease symptoms, misdiagnosis, limited screening initiatives, and late referral to specialists were the primary reasons for delayed diagnoses emphasizing the need for national-level lung cancer screening programs in the MEA region. Screening guidelines recommend low-dose computerized tomography (LDCT) for lung cancer screening in patients with a high risk of malignancy. However, high cost and lack of awareness among the public as well as healthcare providers prevented the judicious use of LDCT in the MEA region. Well-established screening and referral guidelines were available in only a few of the MEA countries and needed to be implemented in others to identify suspected cases early and provide timely intervention thus improving patient outcomes. Conclusions There is a great need for large-scale screening programs, preferably integrated with tobacco-control programs and awareness programs for physicians and patients, which may facilitate higher adherence to lung cancer screening and improve survival outcomes.
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Affiliation(s)
- Ahmed Allehebi
- Department of Oncology, King Faisal Specialist Hospital & Research Centre, Jeddah, Kingdom of Saudi Arabia
| | - Ameen Al-Omair
- Department of Oncology, King Faisal Specialist Hospital & Research Centre, Riyadh, Kingdom of Saudi Arabia
| | - Bassam Mahboub
- Department of Pulmonary Medicine, Dubai Health Authority Hospital, Dubai, United Arab Emirates
| | | | - Mohsen Mokhtar
- Al-Kasr Al-Aini School of Medicine, Cairo University, Cairo, Egypt
| | | | | | - Ugur Selek
- Koc University School of Medicine, Istanbul, Turkey
| | - Humaid O. Al-Shamsi
- Department of Oncology, Burjeel Cancer Institute, Burjeel Medical City, Abu Dhabi, United Arab Emirates
- Emirates Oncology Society, Dubai, United Arab Emirates
- Gulf Medical University, Ajman, United Arab Emirates
- Gulf Cancer Society, Alsafa, Kuwait
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
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Ng LY, Howarth TP, Doss AX, Charakidis M, Karanth NV, Mo L, Heraganahally SS. Significance of lung nodules detected on chest CT among adult Aboriginal Australians - a retrospective descriptive study. J Med Radiat Sci 2024. [PMID: 38516966 DOI: 10.1002/jmrs.783] [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: 11/21/2023] [Accepted: 03/10/2024] [Indexed: 03/23/2024] Open
Abstract
INTRODUCTION There are limited data on chest computed tomography (CT) findings in the assessment of lung nodules among adult Aboriginal Australians. In this retrospective study, we assessed lung nodules among a group of adult Aboriginal Australians in the Northern Territory of Australia. METHODS Patients who underwent at least two chest CT scans between 2012 and 2020 among those referred to undergo lung function testing (spirometry) were included. Chest CT scans were assessed for the number, location, size and morphological characteristics of lung nodules. RESULTS Of the 402 chest CTs assessed, 75 patients (18.7%) had lung nodules, and 57 patients were included in the final analysis with at least two CT scans available for assessment over a median follow-up of 87 weeks. Most patients (68%) were women, with a median age of 58 years and smoking history in 83%. The majority recorded only a single nodule 43 (74%). Six patients (10%) were diagnosed with malignancy, five with primary lung cancer and one with metastatic thyroid cancer. Of the 51 (90%) patients assessed to be benign, 64 nodules were identified, of which 25 (39%) resolved, 38 (59%) remained stable and one (1.8%) enlarged on follow-up. Nodules among patients with malignancy were typically initially larger and enlarged over time, had spiculated margins and were solid, showing no specific lobar predilection. CONCLUSIONS Most lung nodules in Aboriginal Australians are likely to be benign. However, a proportion could be malignant. Further prospective studies are required for prognostication and monitoring of lung nodules in this population.
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Affiliation(s)
- Lai Yun Ng
- Department of Respiratory and Sleep Medicine, Royal Darwin Hospital, Darwin, Northern Territory, Australia
- College of Medicine and Public Health, Flinders University, Darwin, Northern Territory, Australia
| | - Timothy P Howarth
- Darwin Respiratory and Sleep Health, Darwin Private Hospital, Darwin, Northern Territory, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Northern Savo, Finland
| | - Arockia X Doss
- Department of Medical Imaging, Royal Darwin Hospital, Darwin, Northern Territory, Australia
- Curtin Medical School, Bentley, Western Australia, Australia
| | - Michail Charakidis
- Department of Medical Oncology, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Narayan V Karanth
- Department of Medical Oncology, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Lin Mo
- Department of Respiratory and Sleep Medicine, Royal Darwin Hospital, Darwin, Northern Territory, Australia
- College of Medicine and Public Health, Flinders University, Darwin, Northern Territory, Australia
| | - Subash S Heraganahally
- Department of Respiratory and Sleep Medicine, Royal Darwin Hospital, Darwin, Northern Territory, Australia
- College of Medicine and Public Health, Flinders University, Darwin, Northern Territory, Australia
- Darwin Respiratory and Sleep Health, Darwin Private Hospital, Darwin, Northern Territory, Australia
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Ewals LJS, Heesterbeek LJJ, Yu B, van der Wulp K, Mavroeidis D, Funk M, Snijders CCP, Jacobs I, Nederend J, Pluyter JR. The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study. JMIR AI 2024; 3:e52211. [PMID: 38875574 PMCID: PMC11041414 DOI: 10.2196/52211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/14/2023] [Accepted: 02/03/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. OBJECTIVE We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. METHODS In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists' trust in their assessments had changed based on the AI recommendations. RESULTS Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists' confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists' confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. CONCLUSIONS Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists' trust in the AI-CAD system can be impaired. Radiologists' confidence in their assessments was improved by using the AI recommendations.
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Affiliation(s)
- Lotte J S Ewals
- Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, Netherlands
| | | | - Bin Yu
- Research Center for Marketing and Supply Chain Management, Nyenrode Business University, Breukelen, Netherlands
| | - Kasper van der Wulp
- Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, Netherlands
| | | | - Mathias Funk
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Chris C P Snijders
- Department of Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, Eindhoven, Netherlands
| | - Joost Nederend
- Catharina Cancer Institute, Catharina Hospital Eindhoven, Eindhoven, Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Royal Philips, Eindhoven, Netherlands
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Saleh AA, Elghobashy YA, Kasemy ZA, Hegazy A, ALrefai AA. Impact of Dysregulated LINC01559 and LINC01410 Expression on the Diagnosis and Survival of Non-Small Cell Lung Cancer. Biochem Genet 2024:10.1007/s10528-023-10632-1. [PMID: 38265621 DOI: 10.1007/s10528-023-10632-1] [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: 07/19/2023] [Accepted: 12/12/2023] [Indexed: 01/25/2024]
Abstract
Lung cancer is a widely recognized cancer with a very low survival rate, as it is mostly diagnosed at advanced stages. The most prevalent type of lung cancer is non-small cell lung cancer (NSCLC). LncRNAs are widely involved in cancer progression and migration. Therefore, we intended to estimate the circulatory expression levels of LINC01559 and LINC01410 in NSCLC and their roles in tumor prognosis evaluation as less invasive potential markers. The relative expression levels of the plasma cell-free lncRNAs LINC01559 and LINC01410 in seventy patients with NSCLC and seventy healthy subjects as controls were measured by real-time PCR. Enzyme-linked immunosorbent assays were utilized to measure carcinoembryonic antigen (CEA) concentrations. The LINC01559 and LINC01410 expression levels were significantly increased in NSCLC patients versus controls. Both lncRNAs showed good performance in the ROC curve analysis with high sensitivity and specificity for distinguishing patients from controls. LINC01559 had the highest AUC in the ROC curve analysis (0.96, 95 CI% CI: 0.93-0.99) for distinguishing patients from controls, while LINC01410 had the highest AUC (0.77, 95 CI% CI: 0.65-0.89) for differentiating metastatic tumors from nonmetastatic tumors. High expression levels of LINC01410 and LINC01559 were associated with low overall survival (log rank = 47.04 and 28.18, respectively, P < 0.001) and low progression-free survival (log rank = 40.68 and 28.77, respectively (P < 0.001)) and with the presence of metastasis. We suggest that LINC01559 and LINC01410 can be used as valuable, high-performing biomarkers in NSCLC diagnosis and prognosis prediction.
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Affiliation(s)
- Amany A Saleh
- Faculty of Medicine, Medical Biochemistry & Molecular Biology Department, Menoufia University, Shibin Al Kawm, Egypt.
- Medical Surgical Nursing Department, College of Nursing, Taibah University, Medina, Saudi Arabia.
| | - Yasser A Elghobashy
- Faculty of Medicine, Medical Biochemistry & Molecular Biology Department, Menoufia University, Shibin Al Kawm, Egypt
| | - Zeinab A Kasemy
- Faculty of Medicine, Public Health and Community Medicine Department, Menoufia University, Shibin Al Kawm, Egypt
| | - Amira Hegazy
- Faculty of Medicine, Clinical Oncology and Nuclear Medicine Department, Menoufia University, Shibin Al Kawm, Egypt
| | - Abeer A ALrefai
- Faculty of Medicine, Medical Biochemistry & Molecular Biology Department, Menoufia University, Shibin Al Kawm, Egypt
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Yao B, Huang X, Wu F, Li J, He E, Li X, Zhang M, Zhou J, Hong H, Skog S, Wang H. A Novel Model Using Serum Thymidine Kinase 1 and Low-dose Computed Tomography Parameters to Predict Three-year Lung Cancer Risk in People with Pulmonary Nodules: A Retrospective Study. J Cancer 2024; 15:737-746. [PMID: 38213734 PMCID: PMC10777045 DOI: 10.7150/jca.90428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 11/27/2023] [Indexed: 01/13/2024] Open
Abstract
This study was designed to develop a model of serum thymidine kinase 1 protein (STK1p) concentration in combination with low-dose computed tomography (LDCT) to predict the risk of benign pulmonary nodules progressing into lung cancer within three years in a large screening population. The study included a retrospective cohort of 6,841 individuals aged > 30 years who had LDCT-detected pulmonary nodules, but no cancer history or baseline cancer. The outcome was a lung cancer diagnosis recorded within three years after the first detection of pulmonary nodules. The adaptive least absolute shrinkage and selection operator was used to select candidate predictors and fit a logistic model. The model was validated internally by examining discrimination (area under the receiver operating characteristic curve (AUC), calibration (calibration plot)) and net benefit. A web application was developed based on the model. The results showed that the proportion of incident lung cancer cases was 0.79% (n=52). Predictors selected for the model were STK1p and three LDCT parameters (nodule size, type, and count). The AUC of the model was 0.91 (95% confidence interval (CI): 0.86, 0.96). The model had satisfactory discrimination at internal validation (AUC: 0.90 (0.84, 0.96)) and in subgroups (AUC=0.69-0.93). The high-risk group identified by the model exhibited a significantly higher three-year lung cancer risk than the low-risk group (odds ratio (OR): 66.03 (95% CI: 30.49, 162.98)). We concluded that the novel model of STK1p and LDCT parameters together can be used to accurately predict the three-year risk of lung cancer in people with pulmonary nodules.
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Affiliation(s)
- Bei Yao
- First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China
| | - Xiaoyang Huang
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, 518000, China
| | - Fei Wu
- China Health Promotion Foundation, Beijing, 100161, China
| | - Jin Li
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, 518000, China
| | - Ellen He
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, 518000, China
| | - Xiaojing Li
- First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China
| | - Mukun Zhang
- First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China
| | - Ji Zhou
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, 518000, China
| | - Haiou Hong
- First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China
| | - Sven Skog
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, 518000, China
| | - Haidong Wang
- Department of Medicine, Shenzhen Ellen-Sven Precision Medicine Institute, Shenzhen, 518000, China
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Zhou J, Zhao M, Yang Z, Chen L, Liu X. Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis. J Alzheimers Dis 2024; 97:1275-1288. [PMID: 38277290 DOI: 10.3233/jad-230733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
BACKGROUND Alzheimer's disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial intelligence offers new insights into early diagnosis and intervention in AD progression. OBJECTIVE This study, involving 483 AD patients, 756 patients with mild cognitive impairment (MCI), and 968 normal controls (NC), investigated the predictive capability of MRI-based hippocampus volume measurements for AD risk using artificial intelligence and evidence-based medicine. METHODS Utilizing data from ADNI and OASIS-brains databases, three convolutional neural networks (InceptionResNetv2, Densenet169, and SEResNet50) were employed for automated AD classification based on structural MRI imaging. A multitask deep learning model and a densely connected 3D convolutional network were utilized. Additionally, a systematic meta-analysis explored the value of MRI-based hippocampal volume measurement in predicting AD occurrence and progression, drawing on 23 eligible articles from PubMed and Embase databases. RESULTS InceptionResNetv2 outperformed other networks, achieving 99.75% accuracy and 100% AUC for AD-NC classification and 99.16% accuracy and 100% AUC for MCI-NC classification. Notably, at a 512×512 size, InceptionResNetv2 demonstrated a classification accuracy of 94.29% and an AUC of 98% for AD-NC and 97.31% accuracy and 98% AUC for MCI-NC. CONCLUSIONS The study concludes that MRI-based hippocampal volume changes effectively predict AD onset and progression, facilitating early intervention and prevention.
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Affiliation(s)
- Jianguo Zhou
- Department of Radiology, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
| | - Mingli Zhao
- Department of Radiology, The Fourth People's Hospital of Lianyungang Affiliated to Nanjing Medical University Kangda, Lianyungang, China
| | - Zhou Yang
- Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
| | - Liping Chen
- Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
| | - Xiaoli Liu
- Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China
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Sun H, Zhang C, Ouyang A, Dai Z, Song P, Yao J. Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules. Biomed Eng Online 2023; 22:112. [PMID: 38037082 PMCID: PMC10687925 DOI: 10.1186/s12938-023-01180-1] [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: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023] Open
Abstract
PURPOSE To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. METHODS A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). RESULTS The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. CONCLUSIONS A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.
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Affiliation(s)
- Haitao Sun
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Chunling Zhang
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Aimei Ouyang
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Zhengjun Dai
- Scientific Research Department of Huiying Medical Technology Co., Ltd, 66 Xixiaokou Road, Haidian District, Beijing, 100192, China
| | - Peiji Song
- Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, 105 Jiefang Road, Lixia District, Jinan, 250013, Shandong Province, China
| | - Jian Yao
- Medical Imaging Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwuweiqi Road, Huaiyin District, Jinan, 250021, Shandong Province, China.
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12
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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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13
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Ewals LJS, van der Wulp K, van den Borne BEEM, Pluyter JR, Jacobs I, Mavroeidis D, van der Sommen F, Nederend J. The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review. J Clin Med 2023; 12:jcm12103536. [PMID: 37240643 DOI: 10.3390/jcm12103536] [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: 03/15/2023] [Revised: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.
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Affiliation(s)
- Lotte J S Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Ben E E M van den Borne
- Department of Pulmonology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Royal Philips, 5656 AE Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
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miR-4757-3p Inhibited the Migration and Invasion of Lung Cancer Cell via Targeting Wnt Signaling Pathway. JOURNAL OF ONCOLOGY 2023; 2023:6544042. [PMID: 36814555 PMCID: PMC9940952 DOI: 10.1155/2023/6544042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/13/2022] [Accepted: 11/24/2022] [Indexed: 02/17/2023]
Abstract
Lung cancer accounts for the vast majority of cancer-related deaths worldwide, and aberrant miRNA expression is commonly observed as the disease progresses. The current study aimed to determine the role of miR-4757-3p in the development of lung cancer. The real-time PCR test was performed to determine the expression of miR-4757-3p in lung cancer cell lines. miR-4757-3p was downregulated in A549 cells. CCK8 and transwell assays demonstrated that overexpression of miR-4757-3p significantly reduced A549 cell invasion and migration. Bioinformatic analysis by the TargetScan database predicted the possible targets of miR-4757-3p. A luciferase activity test was used to determine the direct relationship between miR-4757-3p, Wnt5a, and Wnt8b. The overexpression of miR-4757-3p drastically inhibited the expression of Wnt5a and Wnt8b. Furthermore, we discovered that silencing Wnt5a and Wnt8b significantly lowered β-catenin expression and hampered invasion and migration. Finally, miR-4757-3p inhibited lung cancer cell migration and invasion by inhibiting the activation of the Wnt signaling pathway. Our study provided evidence that miR-4757-3p could be developed as an indicator or an anticancer target in the clinical application.
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Qiao J, Fan Y, Zhang M, Fang K, Li D, Wang Z. Ensemble framework based on attributes and deep features for benign-malignant classification of lung nodule. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Georgiev A, Chervenkov L, Anastasova V, Kitova T. Comment on "Evaluation of pulmonary nodules by magnetic resonance imaging sequences: which sequence will replace computed tomography?". REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2023; 69:e20221624. [PMID: 37075370 PMCID: PMC10176654 DOI: 10.1590/1806-9282.20221624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 04/21/2023]
Affiliation(s)
- Aleksandar Georgiev
- Medical University Plovdiv, Department of Diagnostic Imaging - Plovdiv, Bulgaria
- Complex Oncology Center Plovdiv, Department of Diagnostic Imaging - Plovdiv, Bulgaria
| | - Lyubomir Chervenkov
- Medical University Plovdiv, Department of Diagnostic Imaging - Plovdiv, Bulgaria
| | - Vania Anastasova
- Medical University Plovdiv, Faculty of Medicine, Department of Propaedeutics of Surgical Diseases, Section of Plastic, Reconstructive and Aesthetic Surgery and Thermal Trauma - Plovdiv, Bulgaria
| | - Tanya Kitova
- Medical University Plovdiv, Department of Human Anatomy, Histology and Embryology - Plovdiv, Bulgaria
- Trakia University, Medical College - Stara Zagora, Bulgaria
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