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Hamabe A, Takemasa I, Ishii M, Okuya K, Hida K, Nishizaki D, Sumii A, Arizono S, Kohno S, Tokunaga K, Nakai H, Sakai Y, Watanabe M. The potential of an artificial intelligence for diagnosing MRI images in rectal cancer: multicenter collaborative trial. J Gastroenterol 2024:10.1007/s00535-024-02133-8. [PMID: 39085490 DOI: 10.1007/s00535-024-02133-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/20/2024] [Indexed: 08/02/2024]
Abstract
BACKGROUND An artificial intelligence-based algorithm we developed, mrAI, satisfactorily segmented the rectal tumor, rectum, and mesorectum from MRI data of rectal cancer patients in an initial study. Herein, we aimed to validate mrAI using an independent dataset. METHODS We utilized MRI images collected in another nationwide research project, "Open versus Laparoscopic Surgery for Advanced Low Rectal Cancer Patients". MRIs from 467 cases with upfront surgery were utilized; six radiologists centralized the MRI evaluations. The diagnostic accuracies of mrAI and the radiologists for tumor depth were compared using pathologic diagnosis as a reference. RESULTS For all cases, centralized diagnosis demonstrated 84.2% sensitivity, 37.7% specificity, and 73.7% accuracy; mrAI exhibited 70.6% sensitivity, 61.3% specificity, and 68.5% accuracy. After limiting MRIs to those acquired by a Philips scanner, with an inter-slice spacing of ≤ 6 mm-both conditions similar to those used in the development of mrAI-the performance of mrAI improved to 76.8% sensitivity, 76.7% specificity, and 76.7% accuracy, while the centralized diagnosis showed 81.8% sensitivity, 36.7% specificity, and 71.3% accuracy. Regarding relapse-free survival, the prognosis for tumors staged ≥ T3 was significantly worse than for tumors staged ≤ T2 (P = 0.0484) in the pathologic diagnosis. While no significant difference was observed between ≥ T3 and ≤ T2 tumors in the centralized diagnosis (P = 0.1510), the prognosis for ≥ T3 was significantly worse in the mrAI diagnosis (P = 0.0318). CONCLUSION Proper imaging conditions for MRI can enhance the accuracy of mrAI, which has the potential to provide feedback to radiologists without overestimating tumor stage.
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Affiliation(s)
- Atsushi Hamabe
- Department of Surgery, Surgical Oncology and Science, Sapporo Medical University, S1 W16, Chuo-Ku, Sapporo, 060-8543, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2-E2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Ichiro Takemasa
- Department of Surgery, Surgical Oncology and Science, Sapporo Medical University, S1 W16, Chuo-Ku, Sapporo, 060-8543, Japan.
| | - Masayuki Ishii
- Department of Surgery, Surgical Oncology and Science, Sapporo Medical University, S1 W16, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Koichi Okuya
- Department of Surgery, Surgical Oncology and Science, Sapporo Medical University, S1 W16, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Koya Hida
- Department of Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Daisuke Nishizaki
- Department of Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Atsuhiko Sumii
- Department of Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shigeki Arizono
- Department of Diagnostic Radiology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Shigeshi Kohno
- Department of Diagnostic Radiology, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Koji Tokunaga
- Department of Diagnostic Radiology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | | | - Yoshiharu Sakai
- Department of Surgery, Osaka Red-Cross Hospital, Osaka, Japan
| | - Masahiko Watanabe
- Department of Surgery, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
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2
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Efetov SK, Cao Y, Zou J, Dorogov AY, Paramonova NB, Tsoy LV, Droshneva IV, Fatyanova AS. Metastasis of colorectal cancer to the uterine body and fallopian tube: case report and literature review. J Surg Case Rep 2024; 2024:rjae400. [PMID: 38859968 PMCID: PMC11163451 DOI: 10.1093/jscr/rjae400] [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: 04/22/2024] [Accepted: 05/29/2024] [Indexed: 06/12/2024] Open
Abstract
Colorectal cancer typically metastasizes to the peritoneum, liver, and lungs. However, metastases to the fallopian tube and uterus are uncommon. This case report delves into this rare occurrence of metastasis and discusses its characteristics, diagnostic methods, and treatments based on an extensive literature review. We present the case of a 61-year-old female patient who underwent her initial hospitalization for da Vinci robotic surgery to address colorectal cancer, stage pT3N0M0. However, during routine postoperative follow-up 6 months later, a localized rectal recurrence was detected. The patient commenced chemoradiotherapy with full response. Subsequently, the patient was readmitted due to pelvic pain again, and a magnetic resonance imaging scan revealed an abnormal mass in the patient's left fallopian tube and uterine corpus, infiltrating the myometrium. Consequently, total hysterectomy with bilateral adnexectomy was performed, along with omentectomy, which confirmed metastatic involvement from rectal cancer upon postoperative pathological examination. This case may inform further diagnosis and treatment of colorectal cancer metastasis to the fallopian tube.
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Affiliation(s)
- Sergey K Efetov
- Department of Faculty Surgery No.2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
| | - Yu Cao
- Department of Faculty Surgery No.2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
| | - Jinqi Zou
- Department of Faculty Surgery No.2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
| | - Anton Y Dorogov
- Department of Faculty Surgery No.2, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
| | - Nina B Paramonova
- Insitute for Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
| | - Larisa V Tsoy
- Insitute for Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
| | - Inna V Droshneva
- P.A. Herzen Moscow Oncology Research Institute, Branch, Nation Medical Radiology Research Center, Ministry of Health of Russia, Moscow, 119435, Russia
| | - Anastasia S Fatyanova
- Department of Oncology, Radiotherapy and Reconstructive Surgery, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russia
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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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Kim M, Park T, Oh BY, Kim MJ, Cho BJ, Son IT. Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review. Ann Coloproctol 2024; 40:13-26. [PMID: 38414120 PMCID: PMC10915525 DOI: 10.3393/ac.2023.00892.0127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/29/2024] Open
Abstract
PURPOSE The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat-ment strategies for patients with rectal cancer. METHODS This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation. RESULTS AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer-ing noninvasive and cost-effective alternatives for identifying genetic mutations. CONCLUSION Image-based AI studies for rectal can-cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.
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Affiliation(s)
- Minsung Kim
- Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Taeyong Park
- Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang, Korea
| | - Bo Young Oh
- Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Min Jeong Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Bum-Joo Cho
- Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang, Korea
| | - Il Tae Son
- Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
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Singh RK, Nayak NP, Behl T, Arora R, Anwer MK, Gulati M, Bungau SG, Brisc MC. Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences. Diagnostics (Basel) 2024; 14:139. [PMID: 38248016 PMCID: PMC11154438 DOI: 10.3390/diagnostics14020139] [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: 11/10/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.
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Affiliation(s)
- Rahul Kumar Singh
- Energy Cluster, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (R.K.S.); (N.P.N.)
| | - Nirlipta Priyadarshini Nayak
- Energy Cluster, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (R.K.S.); (N.P.N.)
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali 140306, Punjab, India
| | - Rashmi Arora
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, Punjab, India;
| | - Md. Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara 1444411, Punjab, India;
- Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology Sydney, Ultimo, NSW 20227, Australia
| | - Simona Gabriela Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania
- Doctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
| | - Mihaela Cristina Brisc
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania;
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Li J, Lin C, Zhu Y, Shao C, Wang T, Chen B. Colorectal cancer cell membrane biomimetic ferroferric oxide nanomaterials for homologous bio-imaging and chemotherapy application. Med Oncol 2023; 40:322. [PMID: 37801170 DOI: 10.1007/s12032-023-02175-7] [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: 06/19/2023] [Accepted: 08/29/2023] [Indexed: 10/07/2023]
Abstract
The research of nanomaterials for bio-imaging and theranostic are very active nowadays with unprecedented advantages in nanomedicine. Homologous targeting and bio-imaging greatly improve the ability of targeted drug delivery and enhance active targeting and treatment ability of nanomedicine for the tumor. In this work, lycorine hydrochloride (LH) and magnetic iron oxide nanoparticles coated with a colorectal cancer (CRC) cell membrane (LH-Fe3O4@M) were prepared, for homologous targeting, magnetic resonance imaging (MRI), and chemotherapy. Results showed that the LH-Fe3O4@M and Fe3O4@M intensity at HT29 tumor was significantly higher than that Fe3O4@PEG, proving the superior selectivity of cancer cell membrane-camouflaged nanomedicine for homologous tumors and the MRI effect of darkening contrast enhancement were remarkable at HT29 tumor. The LH-Fe3O4@M exhibited excellent chemotherapy effect in CRC models as well as LH alone and achieved a high tumor ablation rate but no damage to normal tissues and cells. Therefore, our biomimetic system achieved a homologous targeting, bio-imaging, and efficient therapeutic effect of CRC.
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Affiliation(s)
- Jun Li
- The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, 200120, China
| | - Chenyu Lin
- The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, 200120, China
| | - Yuqian Zhu
- The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, 200120, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, China.
| | - Bingdi Chen
- The Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine, Shanghai, 200120, China.
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7
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Wei MYK, Zhang J, Schmidt R, Miller AS, Yeung JMC. Artificial intelligence (AI) in the management of colorectal cancer: on the horizon? ANZ J Surg 2023; 93:2052-2053. [PMID: 37489622 DOI: 10.1111/ans.18504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 07/26/2023]
Affiliation(s)
- Matthew Y K Wei
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
| | - Junyao Zhang
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
| | - Reuben Schmidt
- Department of Radiology, Western Health, Melbourne, Victoria, Australia
| | - Andrew S Miller
- Department of Colorectal Surgery, Whangarei Hospital, Whangarei, New Zealand
| | - Justin M C Yeung
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Victoria, Australia
- Department of Colorectal Surgery, Western Health, Melbourne, Victoria, Australia
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8
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Gefen R, Garoufalia Z, Horesh N, Freund MR, Emile SH, Parlade A, Berho M, Allende D, DaSilva G, Wexner SD. How reliable is restaging MRI after neoadjuvant therapy in rectal cancer? Colorectal Dis 2023; 25:1631-1637. [PMID: 37376824 DOI: 10.1111/codi.16641] [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: 05/24/2022] [Revised: 01/07/2023] [Accepted: 04/11/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND Despite the pivotal role of magnetic resonance imaging (MRI) in rectal cancer staging and evaluation, the reliability of restaging MRI after neoadjuvant therapy is still debatable. This study aimed to assess the accuracy of restaging MRI by comparing post-neoadjuvant MRI findings with those of the final pathology. METHODS This study was a retrospective review of the medical records of adult rectal cancer patients who had restaging MRI following neoadjuvant therapy and prior to rectal cancer resection in a NAPRC-certified rectal cancer centre between 2016 and 2021. The study compared findings of preoperative, post-neoadjuvant MRI with final pathology relative to T stage, N stage, tumour size, and circumferential resection margin (CRM) status. RESULTS A total of 126 patients were included in the study. We found fair concordance (kappa -0.316) for T stage between restaging MRI and pathology report, and slight concordance for N stage and CRM status (kappa -0.11, kappa = 0.089, respectively). Concordance rates were lower for patients following total neoadjuvant treatment (TNT) or with a low rectal tumour. In total, 73% of patients with positive N pathology status had negative N status in the restaging MRI. Sensitivity and specificity regarding positive CRM in post-neoadjuvant treatment MRI were 45.45% and 70.4%, respectively. CONCLUSION We found low concordance levels between restaging MRI and pathology regarding TN stage and CRM status. Concordance levels were even lower for patients after TNT regimen and with a low rectal tumour. In the era of TNT and watch-and-wait approach, we should not rely solely on restaging MRI to make post-neoadjuvant treatment decisions.
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Affiliation(s)
- Rachel Gefen
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
- Department of General Surgery, Faculty of Medicine, Hadassah Medical Organization, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Zoe Garoufalia
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
| | - Nir Horesh
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
- Department of Surgery and Transplantations, Sheba Medical Centre, Affiliated with the Faculty of Medicine, Tel Aviv University, Ramat Gan, Israel
| | - Michael R Freund
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
- Department of General Surgery, Faculty of Medicine, Shaare Zedek Medical Centre, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sameh Hany Emile
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
- Colorectal Surgery Unit, General Surgery Department, Mansoura University Hospitals, Mansoura, Egypt
| | - Albert Parlade
- Lang Family Department of Imaging, Cleveland Clinic Florida, Weston, Florida, USA
| | - Mariana Berho
- Department of Pathology and Laboratory Medicine, Cleveland Clinic Florida, Weston, Florida, USA
| | - Daniela Allende
- Department of Pathology and Laboratory Medicine, Cleveland Clinic Ohio, Cleveland, Ohio, USA
| | - Giovanna DaSilva
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
| | - Steven D Wexner
- Ellen Leifer Shulman and Steven Shulman Digestive Disease Centre, Cleveland Clinic Florida, Weston, Florida, USA
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Chang J, Zhou H, Li C, Sun J, Wang Q, Li Y, Zhao W. Preparation of PFPE-Based Polymeric Nanoparticles via Polymerization-Induced Self-Assembly as Contrast Agents for 19F MRI. Biomacromolecules 2023. [PMID: 37235210 DOI: 10.1021/acs.biomac.3c00291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Fluorine-19 magnetic resonance imaging (19F MRI) probes have received considerable research interest as imaging contrast agents (CAs), but they remain neglected and underutilized due to the limited fluorine content or poor performance of fluorinated tracers. Here, we present polymeric nanoparticles (NPs) as 19F MRI CAs with a simple synthesis method and promising imaging performance. First, hydrophilic random copolymers were synthesized from oligo(ethylene glycol) methyl ether acrylate and perfluoropolyether methacrylate by reversible addition-fragmentation chain transfer (RAFT) polymerization. The optimal fluorine content, polymer concentration, and cytotoxicity as 19F MRI CAs were investigated in detail. Then, the optimal copolymer was selected as the macromolecular chain transfer agent, and the chain extension was performed with 2-(perfluorooctyl ethyl methacrylate). Subsequently, the NPs with different morphologies, such as ellipsoidal, spherical nanoparticles and vesicles, were prepared in situ by the RAFT-mediated polymerization-induced self-assembly method. In addition, the 19F MRI signal and cytotoxicity studies further confirmed that these polymeric NPs are nontoxic and have great potential as promising 19F MRI CAs for biological applications.
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Affiliation(s)
- Jun Chang
- College of Basic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030001, China
- Key Laboratory of Rubber-plastics, Ministry of Education, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Zhengzhou Rd. 53, CN-266042 Qingdao, China
| | - Huimin Zhou
- College of Basic Medicine, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Chenlong Li
- College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi 030001, China
- Key Laboratory of Rubber-plastics, Ministry of Education, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Zhengzhou Rd. 53, CN-266042 Qingdao, China
| | - Jingjiang Sun
- Key Laboratory of Rubber-plastics, Ministry of Education, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Zhengzhou Rd. 53, CN-266042 Qingdao, China
| | - Qingfu Wang
- Key Laboratory of Rubber-plastics, Ministry of Education, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Zhengzhou Rd. 53, CN-266042 Qingdao, China
| | - Yanan Li
- College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Wei Zhao
- Key Laboratory of Rubber-plastics, Ministry of Education, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Zhengzhou Rd. 53, CN-266042 Qingdao, China
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10
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Chenhui Yao,
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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11
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Yu C, Zhou Z, Liu B, Yao D, Huang Y, Wang P, Li Y. Investigation of trends in gut microbiome associated with colorectal cancer using machine learning. Front Oncol 2023; 13:1077922. [PMID: 36937384 PMCID: PMC10015000 DOI: 10.3389/fonc.2023.1077922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/16/2023] [Indexed: 03/06/2023] Open
Abstract
Background The rapid growth of publications on the gut microbiome and colorectal cancer (CRC) makes it feasible for text mining and bibliometric analysis. Methods Publications were retrieved from the Web of Science. Bioinformatics analysis was performed, and a machine learning-based Latent Dirichlet Allocation (LDA) model was used to identify the subfield research topics. Results A total of 5,696 publications related to the gut microbiome and CRC were retrieved from the Web of Science Core Collection from 2000 to 2022. China and the USA were the most productive countries. The top 25 references, institutions, and authors with the strongest citation bursts were identified. Abstracts from all 5,696 publications were extracted for a text mining analysis that identified the top 50 topics in this field with increasing interest. The colitis animal model, expression of cytokines, microbiome sequencing and 16s, microbiome composition and dysbiosis, and cell growth inhibition were increasingly noticed during the last two years. The 50 most intensively investigated topics were identified and further categorized into four clusters, including "microbiome sequencing and tumor," "microbiome compositions, interactions, and treatment," "microbiome molecular features and mechanisms," and "microbiome and metabolism." Conclusion This bibliometric analysis explores the historical research tendencies in the gut microbiome and CRC and identifies specific topics of increasing interest. The developmental trajectory, along with the noticeable research topics characterized by this analysis, will contribute to the future direction of research in CRC and its clinical translation.
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Guo J, Cao W, Nie B, Qin Q. Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:54-59. [PMID: 36544891 PMCID: PMC9762730 DOI: 10.1109/jtehm.2022.3224021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/31/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022]
Abstract
Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. In this study, we present a composite network that combines deep learning and unsupervised K-means clustering algorithm (RK-net) for automatic processing of medical images. RK-net was more efficient in image refinement compared with manual screening and annotation. The training of a deep learning model for colorectal cancer diagnosis was accelerated by two times with utilization of RK-net-processed images. Better performance was observed in training loss and accuracy achievement as well. RK-net could be useful to refine medical images of the ever-expanding quantity and assist in subsequent construction of the artificial intelligence model.
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Affiliation(s)
- Jirui Guo
- Department of Colorectal SurgeryThe Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China
| | - Wuteng Cao
- Department of RadiologyThe Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China
| | - Bairun Nie
- School of Electrical Computer and Telecommunications EngineeringUniversity of Wollongong Wollongong NSW 2522 Australia
| | - Qiyuan Qin
- Department of Colorectal SurgeryThe Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China
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