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Deng L, Yang J, Zhang M, Zhu K, Zhang J, Ren W, Zhang Y, Jing M, Han T, Zhang B, Zhou J. Predicting lymphovascular invasion in N0 stage non-small cell lung cancer: A nomogram based on Dual-energy CT imaging and clinical findings. Eur J Radiol 2024; 179:111650. [PMID: 39116778 DOI: 10.1016/j.ejrad.2024.111650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 06/14/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024]
Abstract
PURPOSE To construct a nomogram for predicting lymphovascular invasion (LVI) in N0 stage non-small cell lung cancer (NSCLC) using dual-energy computed tomography (DECT) findings combined with clinical findings. METHODS We retrospectively recruited 135 patients with N0 stage NSCLC from two hospitals underwent DECT before surgery and were divided into development cohort (n = 107) and validation cohort (n = 28). The clinical findings (baseline characteristics, biochemical markers, serum tumor markers and Immunohistochemical markers), DECT-derived parameters (iodine concentration [IC], effective atomic number [Eff-Z] and normalized iodine concentration [NIC], iodine enhancement [IE] and NIC ratio [NICr]) and Fractal dimension (FD) were collected and measured. A nomogram was constructed using significant findings to predict LVI in N0 stage NSCLC and was externally validated. RESULTS Multivariable analysis revealed that lymphocyte count (LYMPH, odds ratio [OR]: 3.71, P=0.014), IC in arterial phase (ICa, OR: 1.25, P=0.021), NIC in venous phase (NICv, OR: 587.12, P=0.009) and FD (OR: 0.01, P=0.033) were independent significant factors for predicting LVI in N0 stage NSCLC, and were used to construct a nomogram. The nomogram exhibited robust predictive capabilities in both the development and validation cohort, with AUCs of 0.819 (95 % CI: 72.6-90.4) and 0.844 (95 % CI: 68.2-95.8), respectively. The calibration plots showed excellent agreement between the predicted probabilities and the actual rates of positive LVI, on external validation. CONCLUSIONS Combination of clinical and DECT imaging findings could aid in predicting LVI in N0 stage NSCLC using significant findings of LYMPH, ICa, NICv and FD.
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Affiliation(s)
- Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Jingjing Yang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Mingtao Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730000, China; Department of Orthopedics, Lanzhou University Second Hospital, 730000, China
| | - Kaibo Zhu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Junfu Zhang
- Department of Magnetic Resonance, The People's Hospital of Linxia, linxia 731100, China
| | - Wei Ren
- GE Healthcare, Computed Tomography Research Center, Beijing, PR China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China.
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Nardi M, Arceo MD, Ladenheim RI. [Analysis of the Argentine residency system from the paradigm of Complexity Sciences]. REVISTA DE LA FACULTAD DE CIENCIAS MÉDICAS 2023; 80:163-167. [PMID: 37402293 PMCID: PMC10443409 DOI: 10.31053/1853.0605.v80.n2.39843] [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: 03/31/2023] [Accepted: 05/07/2023] [Indexed: 07/06/2023] Open
Abstract
Aim The objective is to carry out an analysis of the system of residences in Health in Argentina from the Theory of Complexity, in order to improve the understanding of the reality of this system, allowing an analysis to be carried out from another perspective different from the traditional. Source and synthesis of data In this review, the properties and characteristics of the residence system are analyzed according to the new paradigm of the Science of Complexity. For those who make decisions about the residential training of young professionals, full knowledge of the system in a holistic way and reflection on the characteristics of an open system applied to the residential system is of great importance and, it gives (or can give off) great benefits and possible improvements for those who live the day to day of these training programs. Conclusions It is important to mention the ultimate benefit that knowledge of the study system analyzed here has or can have: the possibility of multidisciplinarity, as one more step in the evolution of this type of system.
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Alexiou A, Tsagkaris C, Chatzichronis S, Koulouris A, Haranas I, Gkigkitzis I, Zouganelis G, Mukerjee N, Maitra S, Jha NK, Batiha GES, Kamal MA, Nikolaou M, Ashraf GM. The Fractal Viewpoint of Tumors and Nanoparticles. Curr Med Chem 2023; 30:356-370. [PMID: 35927901 DOI: 10.2174/0929867329666220801152347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 02/08/2023]
Abstract
Even though the promising therapies against cancer are rapidly improved, the oncology patients population has seen exponential growth, placing cancer in 5th place among the ten deadliest diseases. Efficient drug delivery systems must overcome multiple barriers and maximize drug delivery to the target tumors, simultaneously limiting side effects. Since the first observation of the quantum tunneling phenomenon, many multidisciplinary studies have offered quantum-inspired solutions to optimized tumor mapping and efficient nanodrug design. The property of a wave function to propagate through a potential barrier offer the capability of obtaining 3D surface profiles using imaging of individual atoms on the surface of a material. The application of quantum tunneling on a scanning tunneling microscope offers an exact surface roughness mapping of tumors and pharmaceutical particles. Critical elements to cancer nanotherapeutics apply the fractal theory and calculate the fractal dimension for efficient tumor surface imaging at the atomic level. This review study presents the latest biological approaches to cancer management based on fractal geometry.
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Affiliation(s)
- Athanasios Alexiou
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia.,AFNP Med, 1030 Wien, Austria
| | - Christos Tsagkaris
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia.,European Student Think Tank, Public Health and Policy Working Group, 1058, Amsterdam, Netherlands
| | - Stylianos Chatzichronis
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Andreas Koulouris
- Thoracic Oncology Center, Theme Cancer, Karolinska University Hospital, 17177 Stockholm, Sweden.,Faculty of Medicine, University of Crete, 70013 Heraklion, Greece
| | - Ioannis Haranas
- Department of Physics and Computer Science, Wilfrid Laurier University, Waterloo, ON, N2L-3C5, Canada
| | - Ioannis Gkigkitzis
- NOVA Department of Mathematics, 8333 Little River Turnpike, Annandale, VA 22003 USA
| | - Georgios Zouganelis
- Human Sciences Research Centre, College of Life and Natural Sciences, University of Derby, East Midlands, DE22 1GB England, UK
| | - Nobendu Mukerjee
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia.,Department of Microbiology; Ramakrishna Mission Vivekananda Centenary College, Akhil Mukherjee Rd, Chowdhary Para, Rahara, Khardaha, West Bengal, Kolkata- 700118, India
| | - Swastika Maitra
- Department of Microbiology, Adamas University, Kolkata, India
| | - Niraj Kumar Jha
- Department of Biotechnology, School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh, 201310, India.,Department of Biotechnology, School of Applied & Life Sciences (SALS), Uttaranchal University, Dehradun 248007, India.,Department of Biotechnology Engineering and Food Technology, Chandigarh University, Mohali, 140413, India
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, AlBeheira, Egypt
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.,King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh.,Enzymoics, 7 Peterlee place, Hebersham, NSW 2770; Novel Global Community Educational Foundation, Australia
| | - Michail Nikolaou
- 1st Oncology Department, "Saint Savas" Anticancer, Oncology Hospital, 11522 Athens, Greece
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Michallek F, Sartoris R, Beaufrère A, Dioguardi Burgio M, Cauchy F, Cannella R, Paradis V, Ronot M, Dewey M, Vilgrain V. Differentiation of hepatocellular adenoma by subtype and hepatocellular carcinoma in non-cirrhotic liver by fractal analysis of perfusion MRI. Insights Imaging 2022; 13:81. [PMID: 35482151 PMCID: PMC9050986 DOI: 10.1186/s13244-022-01223-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background To investigate whether fractal analysis of perfusion differentiates hepatocellular adenoma (HCA) subtypes and hepatocellular carcinoma (HCC) in non-cirrhotic liver by quantifying perfusion chaos using four-dimensional dynamic contrast-enhanced magnetic resonance imaging (4D-DCE-MRI). Results A retrospective population of 63 patients (47 female) with histopathologically characterized HCA and HCC in non-cirrhotic livers was investigated. Our population consisted of 13 hepatocyte nuclear factor (HNF)-1α-inactivated (H-HCAs), 7 β-catenin-exon-3-mutated (bex3-HCAs), 27 inflammatory HCAs (I-HCAs), and 16 HCCs. Four-dimensional fractal analysis was applied to arterial, portal venous, and delayed phases of 4D-DCE-MRI and was performed in lesions as well as remote liver tissue. Diagnostic accuracy of fractal analysis was compared to qualitative MRI features alone and their combination using multi-class diagnostic accuracy testing including kappa-statistics and area under the receiver operating characteristic curve (AUC). Fractal analysis allowed quantification of perfusion chaos, which was significantly different between lesion subtypes (multi-class AUC = 0.90, p < 0.001), except between I-HCA and HCC. Qualitative MRI features alone did not allow reliable differentiation between HCA subtypes and HCC (κ = 0.35). However, combining qualitative MRI features and fractal analysis reliably predicted the histopathological diagnosis (κ = 0.89) and improved differentiation of high-risk lesions (i.e., HCCs, bex3-HCAs) and low-risk lesions (H-HCAs, I-HCAs) from sensitivity and specificity of 43% (95% confidence interval [CI] 23–66%) and 47% (CI 32–64%) for qualitative MRI features to 96% (CI 78–100%) and 68% (CI 51–81%), respectively, when adding fractal analysis. Conclusions Combining qualitative MRI features with fractal analysis allows identification of HCA subtypes and HCCs in patients with non-cirrhotic livers and improves differentiation of lesions with high and low risk for malignant transformation. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01223-6.
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Affiliation(s)
- Florian Michallek
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
| | - Riccardo Sartoris
- Université de Paris, CRI, U1149, Paris, France.,Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - Aurélie Beaufrère
- Université de Paris, CRI, U1149, Paris, France.,Department of Pathology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - Marco Dioguardi Burgio
- Université de Paris, CRI, U1149, Paris, France.,Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - François Cauchy
- Department of HBP Surgery, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - Roberto Cannella
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France.,Section of Radiology - BiND, University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy.,Department of Health Promotion Sciences Maternal and Infant Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, 90127, Palermo, Italy
| | - Valérie Paradis
- Department of Pathology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - Maxime Ronot
- Université de Paris, CRI, U1149, Paris, France.,Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
| | - Marc Dewey
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.,DKTK (German Cancer Consortium), Partner Site, Berlin, Germany
| | - Valérie Vilgrain
- Université de Paris, CRI, U1149, Paris, France.,Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
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Michallek F, Huisman H, Hamm B, Elezkurtaj S, Maxeiner A, Dewey M. Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study. Eur Radiol 2021; 32:3236-3247. [PMID: 34913991 PMCID: PMC9038862 DOI: 10.1007/s00330-021-08394-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 09/28/2021] [Accepted: 10/09/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Multiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade. METHODS We retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements. RESULTS Fractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2-5) cancer with a sensitivity of 91% (confidence interval [CI]: 83-96%) and a specificity of 86% (CI: 73-94%). FD correlated linearly with ISUP groups (r2 = 0.874, p < 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1-4 (p ≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUCFD = 0.97 versus AUCADC = 0.77, p < 0.001). CONCLUSION Fractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors. KEY POINTS • In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1-4). • Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83-96%) and a specificity of 86% (73-94%). • Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading.
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Affiliation(s)
- Florian Michallek
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
| | - Henkjan Huisman
- Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Bernd Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Sefer Elezkurtaj
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Andreas Maxeiner
- Department of Urology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Marc Dewey
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
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