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de Oliveira Avellar W, Ferreira ÉA, Aran V. Artificial Intelligence and cancer: Profile of registered clinical trials. J Cancer Policy 2024; 42:100503. [PMID: 39242028 DOI: 10.1016/j.jcpo.2024.100503] [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: 07/17/2024] [Revised: 08/06/2024] [Accepted: 08/31/2024] [Indexed: 09/09/2024]
Abstract
Artificial Intelligence (AI) has made significant strides due to advancements in processing algorithms and data availability. Recent years have shown a resurgence in AI, driven by breakthroughs in deep machine learning. AI has attracted particular interest in the medical sector, especially in the field of personalized medicine, which for example uses large-scale genomic and molecular data to predict individual patient treatment responses. The applications of AI in disease diagnosis, monitoring, and treatment are expanding rapidly, leading to a growing number of registered trials. Therefore, this study aimed to identify and evaluate clinical trials registered between January 1st 2016, and September 30th 2023 that connect AI and cancer. Our findings show that the number of clinical trials linking AI with cancer research has grown significantly compared to other diseases, with colorectal and breast tumour types showing the highest number of registered trials. The most frequent intervention was disease diagnosis and monitoring. Regarding countries, China and the United States hold the highest numbers of registered trials. In conclusion, oncology is a field with a great interest in AI, where the developed countries are leading the studies in this field. Unfortunately, developing countries are still crawling in this aspect and government policies should be made to improve that area.
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Affiliation(s)
- William de Oliveira Avellar
- Division of Clinical Research and Technological Development, Brazilian National Cancer Institute (INCA), Rua André Cavalcanti 37, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil
| | - Édria Aparecida Ferreira
- Division of Clinical Research and Technological Development, Brazilian National Cancer Institute (INCA), Rua André Cavalcanti 37, Bairro de Fátima, Rio de Janeiro 20231-050, Brazil
| | - Veronica Aran
- Instituto Estadual do Cérebro Paulo Niemeyer (IECPN), Rua do Rezende, 156-Centro, Rio de Janeiro 20231-092, Brazil; Programa de Pós-Graduação em Anatomia Patológica, Faculdade de Medicina, Universidade Federal do Rio de Janeiro (UFRJ), Av. Rodolpho Paulo Rocco 225, Rio de Janeiro 21941-905, Brazil.
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2
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Khalid M, Deivasigamani S, V S, Rajendran S. An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images. Sci Rep 2024; 14:19109. [PMID: 39154091 PMCID: PMC11330491 DOI: 10.1038/s41598-024-70117-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024] Open
Abstract
The second most common type of malignant tumor worldwide is colorectal cancer. Histopathology image analysis offers crucial data for the clinical diagnosis of colorectal cancer. Currently, deep learning techniques are applied to enhance cancer classification and tumor localization in histopathological image analysis. Moreover, traditional deep learning techniques might loss integrated information in the image while evaluating thousands of patches recovered from whole slide images (WSIs). This research proposes a novel colorectal cancer detection network (CCDNet) that combines coordinate attention transformer with atrous convolution. CCDNet first denoises the input histopathological image using a Wiener based Midpoint weighted non-local means filter (WMW-NLM) for guaranteeing precise diagnoses and maintain image features. Also, a novel atrous convolution with coordinate attention transformer (AConvCAT) is introduced, which successfully combines the advantages of two networks to classify colorectal tissue at various scales by capturing local and global information. Further, coordinate attention model is integrated with a Cross-shaped window (CrSWin) transformer for capturing tiny changes in colorectal tissue from multiple angles. The proposed CCDNet achieved accuracy rates of 98.61% and 98.96%, on the colorectal histological image and NCT-CRC-HE-100 K datasets correspondingly. The comparison analysis demonstrates that the suggested framework performed better than the most advanced methods already in use. In hospitals, clinicians can use the proposed CCDNet to verify the diagnosis.
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Affiliation(s)
- Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, 21955, Makkah, Saudi Arabia
| | - Sugitha Deivasigamani
- Department of Computer Science and Engineering, University College of Engineering, A Constituent College of Anna University, Thirukkuvalai, Chennai, India
| | - Sathiya V
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India
| | - Surendran Rajendran
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India.
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3
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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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4
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Kiran N, Yashaswini C, Maheshwari R, Bhattacharya S, Prajapati BG. Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies. ACS Pharmacol Transl Sci 2024; 7:967-990. [PMID: 38633600 PMCID: PMC11019743 DOI: 10.1021/acsptsci.4c00008] [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: 01/10/2024] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 04/19/2024]
Abstract
Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.
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Affiliation(s)
- Neelakanta
Sarvashiva Kiran
- Department
of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India
| | - Chandrashekar Yashaswini
- Department
of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India
| | - Rahul Maheshwari
- School
of Pharmacy and Technology Management, SVKM’s
Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-University, Green Industrial Park, TSIIC,, Jadcherla, Hyderabad 509301, India
| | - Sankha Bhattacharya
- School
of Pharmacy and Technology Management, SVKM’S
NMIMS Deemed-to-be University, Shirpur, Maharashtra 425405, India
| | - Bhupendra G. Prajapati
- Shree.
S. K. Patel College of Pharmaceutical Education and Research, Ganpat University, Kherva, Gujarat 384012, India
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Carter D, Bykhovsky D, Hasky A, Mamistvalov I, Zimmer Y, Ram E, Hoffer O. Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds. Tech Coloproctol 2024; 28:44. [PMID: 38561492 PMCID: PMC10984882 DOI: 10.1007/s10151-024-02917-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/06/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images. METHODS A transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation. RESULTS The DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer. CONCLUSIONS This research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.
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Affiliation(s)
- D Carter
- Department of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - D Bykhovsky
- Electrical and Electronics Engineering Department, Shamoon College of Engineering, Beer-Sheba, Israel
| | - A Hasky
- School of Electrical Engineering, Afeka College of Engineering, Tel Aviv, Israel
| | - I Mamistvalov
- School of Electrical Engineering, Afeka College of Engineering, Tel Aviv, Israel
| | - Y Zimmer
- School of Medical Engineering, Afeka College of Engineering, Tel Aviv, Israel
| | - E Ram
- Department of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - O Hoffer
- School of Electrical Engineering, Afeka College of Engineering, Tel Aviv, Israel
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6
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Atarere J, Naqvi H, Haas C, Adewunmi C, Bandaru S, Allamneni R, Ugonabo O, Egbo O, Umoren M, Kanth P. Applicability of Online Chat-Based Artificial Intelligence Models to Colorectal Cancer Screening. Dig Dis Sci 2024; 69:791-797. [PMID: 38267726 DOI: 10.1007/s10620-024-08274-3] [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: 10/21/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Over the past year, studies have shown potential in the applicability of ChatGPT in various medical specialties including cardiology and oncology. However, the application of ChatGPT and other online chat-based AI models to patient education and patient-physician communication on colorectal cancer screening has not been critically evaluated which is what we aimed to do in this study. METHODS We posed 15 questions on important colorectal cancer screening concepts and 5 common questions asked by patients to the 3 most commonly used freely available artificial intelligence (AI) models. The responses provided by the AI models were graded for appropriateness and reliability using American College of Gastroenterology guidelines. The responses to each question provided by an AI model were graded as reliably appropriate (RA), reliably inappropriate (RI) and unreliable. Grader assessments were validated by the joint probability of agreement for two raters. RESULTS ChatGPT and YouChat™ provided RA responses to the questions posed more often than BingChat. There were two questions that > 1 AI model provided unreliable responses to. ChatGPT did not provide references. BingChat misinterpreted some of the information it referenced. The age of CRC screening provided by YouChat™ was not consistently up-to-date. Inter-rater reliability for 2 raters was 89.2%. CONCLUSION Most responses provided by AI models on CRC screening were appropriate. Some limitations exist in their ability to correctly interpret medical literature and provide updated information in answering queries. Patients should consult their physicians for context on the recommendations made by these AI models.
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Affiliation(s)
- Joseph Atarere
- Department of Medicine, MedStar Health, 201 East University Pkwy, Baltimore, MD, 21218, USA.
- Department of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Haider Naqvi
- Department of Medicine, MedStar Health, 201 East University Pkwy, Baltimore, MD, 21218, USA
| | - Christopher Haas
- Department of Medicine, MedStar Health, 201 East University Pkwy, Baltimore, MD, 21218, USA
| | - Comfort Adewunmi
- Division of Geriatrics and Gerontology, Emory University School of Medicine, Atlanta, GA, USA
| | - Sumanth Bandaru
- Department of Medicine, MedStar Health, 201 East University Pkwy, Baltimore, MD, 21218, USA
| | - Rakesh Allamneni
- Department of Medicine, MedStar Health, 201 East University Pkwy, Baltimore, MD, 21218, USA
| | - Onyinye Ugonabo
- Department of Medicine, Marshall University Joan C. Edwards School of Medicine, Huntington, WV, USA
| | - Olachi Egbo
- Department of Medicine, Aurora Medical Center, Oshkosh, WI, USA
| | - Mfoniso Umoren
- Division of Gastroenterology, Georgetown University Hospital, Washington, DC, USA
| | - Priyanka Kanth
- Division of Gastroenterology, Georgetown University Hospital, Washington, DC, USA
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Zhang K, He Q, Cao Q, Chuan J, Qin A, Tang L, Zhang X, Xiao C, Zhu B, Hu M, Chang L, Bu ZX, Fu L, Yang T, Wang Y, Liu W. Evaluating the clinical performance of SDC2/NDRG4 methylation for colorectal cancer detection. Epigenomics 2024; 16:93-108. [PMID: 38226561 DOI: 10.2217/epi-2023-0290] [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] [Indexed: 01/17/2024] Open
Abstract
Purpose: The performance and clinical accuracy of combined SDC2/NDRG4 methylation were evaluated in diagnosing colorectal cancer (CRC) and advanced adenoma. Methods: A total of 2333 participants were enrolled to assess the sensitivity and specificity of biomarkers in diagnosing CRC in a multicenter clinical trial through feces DNA methylation tests. Results: SDC2/NDRG4 methylation showed excellent performance for CRC detection in biomarker research and the real world. Its sensitivity for detecting CRC, early CRC and advanced adenoma were 92.06%, 91.45% and 62.61%, respectively. Its specificity was 94.29%, with a total coincidence rate of 88.28%. When interference samples were included, the specificity was still good (82.61%). Therefore, the SDC2/NDRG4 methylation test showed excellent performance in detecting CRC and advanced adenoma under clinical application.
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Affiliation(s)
- Ke Zhang
- Department of General Surgery, Xiangya Hospital Central South University, Changsha, 410000, China
| | - Qing He
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Qin Cao
- Department of Gastroenterology, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China
| | - Jun Chuan
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Ang Qin
- Department of Endoscope Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Lin Tang
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Xinyue Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410000, China
| | - Changhe Xiao
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Biyin Zhu
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Meiling Hu
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Lei Chang
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Zhong Xin Bu
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Lanqi Fu
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Ting Yang
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
| | - Yu Wang
- GeneTalks Biotech Co., Ltd, Changsha, Hunan, 410000, PR China
- School of Life Sciences & Technology, Tongji University, Shanghai, 200092, China
| | - Weidong Liu
- Department of General Surgery, Xiangya Hospital Central South University, Changsha, 410000, China
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Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1286-1300. [PMID: 38213536 PMCID: PMC10776591 DOI: 10.37349/etat.2023.00197] [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: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 01/13/2024] Open
Abstract
Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.
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Affiliation(s)
- Kriti Das
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Maanvi Paltani
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Pankaj Kumar Tripathi
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| | - Rajnish Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Saniya Verma
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Subodh Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
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Gayathri R, Suchand Sandeep CS, Vijayan C, Murukeshan VM. Random Lasing for Bimodal Imaging and Detection of Tumor. BIOSENSORS 2023; 13:1003. [PMID: 38131763 PMCID: PMC10742073 DOI: 10.3390/bios13121003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
The interaction of light with biological tissues is an intriguing area of research that has led to the development of numerous techniques and technologies. The randomness inherent in biological tissues can trap light through multiple scattering events and provide optical feedback to generate random lasing emission. The emerging random lasing signals carry sensitive information about the scattering dynamics of the medium, which can help in identifying abnormalities in tissues, while simultaneously functioning as an illumination source for imaging. The early detection and imaging of tumor regions are crucial for the successful treatment of cancer, which is one of the major causes of mortality worldwide. In this paper, a bimodal spectroscopic and imaging system, capable of identifying and imaging tumor polyps as small as 1 mm2, is proposed and illustrated using a phantom sample for the early diagnosis of tumor growth. The far-field imaging capabilities of the developed system can enable non-contact in vivo inspections. The integration of random lasing principles with sensing and imaging modalities has the potential to provide an efficient, minimally invasive, and cost-effective means of early detection and treatment of various diseases, including cancer.
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Affiliation(s)
- R. Gayathri
- Centre for Optical and Laser Engineering (COLE), School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore; (R.G.); (C.S.S.S.)
| | - C. S. Suchand Sandeep
- Centre for Optical and Laser Engineering (COLE), School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore; (R.G.); (C.S.S.S.)
| | - C. Vijayan
- Department of Physics, Indian Institute of Technology Madras (IITM), Chennai 600036, India;
| | - V. M. Murukeshan
- Centre for Optical and Laser Engineering (COLE), School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore; (R.G.); (C.S.S.S.)
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10
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Ding M, Yan J, Chao G, Zhang S. Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review). Oncol Rep 2023; 50:199. [PMID: 37772392 DOI: 10.3892/or.2023.8636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/07/2023] [Indexed: 09/30/2023] Open
Abstract
Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.
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Affiliation(s)
- Menglu Ding
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Junbin Yan
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Guanqun Chao
- Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, P.R. China
| | - Shuo Zhang
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
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11
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Gabralla LA, Hussien AM, AlMohimeed A, Saleh H, Alsekait DM, El-Sappagh S, Ali AA, Refaat Hassan M. Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence. Diagnostics (Basel) 2023; 13:2939. [PMID: 37761306 PMCID: PMC10529133 DOI: 10.3390/diagnostics13182939] [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: 07/08/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI).
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Affiliation(s)
- Lubna Abdelkareim Gabralla
- Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ali Mohamed Hussien
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
| | - Abdulaziz AlMohimeed
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Deema Mohammed Alsekait
- Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 34511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Moatamad Refaat Hassan
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
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12
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Li W, Zhang Y, Chen F. ChatGPT in Colorectal Surgery: A Promising Tool or a Passing Fad? Ann Biomed Eng 2023; 51:1892-1897. [PMID: 37162695 DOI: 10.1007/s10439-023-03232-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/11/2023]
Abstract
Colorectal surgery is a specialized branch of surgery that involves the diagnosis and treatment of conditions affecting the colon, rectum, and anus. In the recent years, the use of artificial intelligence (AI) has gained considerable interest in various medical specialties, including surgery. Chatbot Generative Pre-Trained Transformer (ChatGPT), an AI-based chatbot developed by OpenAI, has shown great potential in improving the quality of healthcare delivery by providing accurate and timely information to both patients and healthcare professionals. In this paper, we investigate the potential application of ChatGPT in colorectal surgery. We also discuss the potential advantages and challenges associated with the implementation of ChatGPT in the surgical setting. Furthermore, we address the socio-ethical implications of utilizing ChatGPT in healthcare. This includes concerns over patient privacy, liability, and the potential impact on the doctor-patient relationship. Our findings suggest that ChatGPT has the potential to revolutionize the field of colorectal surgery by providing personalized and precise medical information, reducing errors and complications, and improving patient outcomes.
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Affiliation(s)
- Wenbo Li
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Yinxu Zhang
- Department of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, 121001, China
| | - Fengmin Chen
- Department of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, 121001, China.
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13
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Asadnia A, Nazari E, Goshayeshi L, Zafari N, Moetamani-Ahmadi M, Goshayeshi L, Azari H, Pourali G, Khalili-Tanha G, Abbaszadegan MR, Khojasteh-Leylakoohi F, Bazyari M, Kahaei MS, Ghorbani E, Khazaei M, Hassanian SM, Gataa IS, Kiani MA, Peters GJ, Ferns GA, Batra J, Lam AKY, Giovannetti E, Avan A. The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach. Cancers (Basel) 2023; 15:4300. [PMID: 37686578 PMCID: PMC10486397 DOI: 10.3390/cancers15174300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.
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Affiliation(s)
- Alireza Asadnia
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Elham Nazari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19839-69411, Iran;
| | - Ladan Goshayeshi
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran;
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48954, Iran;
| | - Nima Zafari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Mehrdad Moetamani-Ahmadi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
| | - Lena Goshayeshi
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48954, Iran;
| | - Haneih Azari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Mohammad Reza Abbaszadegan
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Fatemeh Khojasteh-Leylakoohi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - MohammadJavad Bazyari
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran;
| | - Mir Salar Kahaei
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
| | - Elnaz Ghorbani
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | | | - Mohammad Ali Kiani
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Godefridus J. Peters
- Department of Biochemistry, Medical University of Gdansk, 80-211 Gdansk, Poland;
- Cancer Center Amsterdam, Amsterdam U.M.C., VU University Medical Center (VUMC), Department of Medical Oncology, 1081 HV Amsterdam, The Netherlands
| | - Gordon A. Ferns
- Brighton & Sussex Medical School, Department of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK;
| | - Jyotsna Batra
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
| | - Alfred King-yin Lam
- Pathology, School of Medicine and Dentistry, Gold Coast Campus, Griffith University, Gold Coast, QLD 4222, Australia;
| | - Elisa Giovannetti
- Cancer Center Amsterdam, Amsterdam U.M.C., VU University Medical Center (VUMC), Department of Medical Oncology, 1081 HV Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start Up Unit, Fondazione Pisana per La Scienza, 56017 Pisa, Italy
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran;
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
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14
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García-Domínguez A, Galván-Tejada CE, Magallanes-Quintanar R, Gamboa-Rosales H, Curiel IG, Peralta-Romero J, Cruz M. Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation. J Diabetes Res 2023; 2023:9713905. [PMID: 37404324 PMCID: PMC10317588 DOI: 10.1155/2023/9713905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/08/2023] [Accepted: 06/18/2023] [Indexed: 07/06/2023] Open
Abstract
The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment.
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Affiliation(s)
- Antonio García-Domínguez
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Carlos E. Galván-Tejada
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Rafael Magallanes-Quintanar
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Hamurabi Gamboa-Rosales
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Irma González Curiel
- Academic Unit of Chemical Sciences, Autonomous University of Zacatecas, Juarez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Jesús Peralta-Romero
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center Siglo XXI, Mexican Social Security Institute, Mexico City, Mexico
| | - Miguel Cruz
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center Siglo XXI, Mexican Social Security Institute, Mexico City, Mexico
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15
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Scharitzer M, Lampichler K, Popp S, Mang T. [Computed tomography and magnetic resonance imaging of colonic diseases]. RADIOLOGIE (HEIDELBERG, GERMANY) 2023:10.1007/s00117-023-01150-7. [PMID: 37219728 DOI: 10.1007/s00117-023-01150-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 04/13/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Early diagnosis of a luminal colonic disease is of essential clinical importance to start timely optimised therapy and detect complications early. OBJECTIVES This paper aims to provide an overview of the use of radiological methods in diagnosing neoplastic and inflammatory luminal diseases of the colon. Characteristic morphological features are discussed and compared. MATERIALS AND METHODS Based on an extensive literature review, the current state of knowledge regarding the imaging diagnosis of luminal pathologies of the colon and their importance in patient management is presented. RESULTS Technological advances in imaging have made the diagnosis of neoplastic and inflammatory colonic diseases using abdominal computed tomography and magnetic resonance imaging the established standard. Imaging is performed as part of the initial diagnosis in clinically symptomatic patients, to exclude complications, as a follow-up assessment under therapy and as an optional screening method in asymptomatic individuals. CONCLUSIONS Accurate knowledge of the radiological manifestations of the numerous luminal disease patterns, the typical distribution pattern and characteristic bowel wall changes are essential to improve diagnostic decision-making.
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Affiliation(s)
- Martina Scharitzer
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Waehringer Guertel 18-20, 1090, Wien, Österreich.
| | - Katharina Lampichler
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Waehringer Guertel 18-20, 1090, Wien, Österreich
| | - Sabine Popp
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Waehringer Guertel 18-20, 1090, Wien, Österreich
| | - Thomas Mang
- Universitätsklinik für Radiologie und Nuklearmedizin, Medizinische Universität Wien, Waehringer Guertel 18-20, 1090, Wien, Österreich
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16
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Karnachoriti M, Stathopoulos I, Kouri M, Spyratou E, Orfanoudakis S, Lykidis D, Lambropoulou Μ, Danias N, Arkadopoulos N, Efstathopoulos EP, Raptis YS, Seimenis I, Kontos AG. Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122852. [PMID: 37216817 DOI: 10.1016/j.saa.2023.122852] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/29/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.
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Affiliation(s)
- M Karnachoriti
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - I Stathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - M Kouri
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece; Medical Physics Program, University of Massachusetts Lowell, MA 01854, United States
| | - E Spyratou
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - S Orfanoudakis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece
| | - D Lykidis
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - Μ Lambropoulou
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - N Danias
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - N Arkadopoulos
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - E P Efstathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Y S Raptis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece
| | - I Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - A G Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece.
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17
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Lobbes LA, Schütze MA, Droeser R, Arndt M, Pozios I, Lauscher JC, Hering NA, Weixler B. Muscarinic Acetylcholine Receptor M3 Expression and Survival in Human Colorectal Carcinoma-An Unexpected Correlation to Guide Future Treatment? Int J Mol Sci 2023; 24:ijms24098198. [PMID: 37175905 PMCID: PMC10179005 DOI: 10.3390/ijms24098198] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/24/2023] [Accepted: 04/30/2023] [Indexed: 05/15/2023] Open
Abstract
Muscarinic acetylcholine receptor M3 (M3R) has repeatedly been shown to be prominently expressed in human colorectal cancer (CRC), playing roles in proliferation and cell invasion. Its therapeutic targetability has been suggested in vitro and in animal models. We aimed to investigate the clinical role of MR3 expression in CRC for human survival. Surgical tissue samples from 754 CRC patients were analyzed for high or low immunohistochemical M3R expression on a clinically annotated tissue microarray (TMA). Immunohistochemical analysis was performed for established immune cell markers (CD8, TIA-1, FOXP3, IL 17, CD16 and OX 40). We used Kaplan-Meier curves to evaluate patients' survival and multivariate Cox regression analysis to evaluate prognostic significance. High M3R expression was associated with increased survival in multivariate (hazard ratio (HR) = 0.52; 95% CI = 0.35-0.78; p = 0.001) analysis, as was TIA-1 expression (HR = 0.99; 95% CI = 0.94-0.99; p = 0.014). Tumors with high M3R expression were significantly more likely to be grade 2 compared to tumors with low M3R expression (85.7% vs. 67.1%, p = 0.002). The 5-year survival analysis showed a trend of a higher survival rate in patients with high M3R expression (46%) than patients with low M3R expression CRC (42%) (p = 0.073). In contrast to previous in vitro and animal model findings, this study demonstrates an increased survival for CRC patients with high M3R expression. This evidence is highly relevant for translation of basic research findings into clinically efficient treatments.
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Affiliation(s)
- Leonard A Lobbes
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Marcel A Schütze
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Raoul Droeser
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, CH-4058 Basel, Switzerland
| | - Marco Arndt
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Ioannis Pozios
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Johannes C Lauscher
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Nina A Hering
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Benjamin Weixler
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
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