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Lizano M, Carrillo-García A, De La Cruz-Hernández E, Castro-Muñoz LJ, Contreras-Paredes A. Promising predictive molecular biomarkers for cervical cancer (Review). Int J Mol Med 2024; 53:50. [PMID: 38606495 PMCID: PMC11090266 DOI: 10.3892/ijmm.2024.5374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Cervical cancer (CC) constitutes a serious public health problem. Vaccination and screening programs have notably reduced the incidence of CC worldwide by >80%; however, the mortality rate in low‑income countries remains high. The staging of CC is a determining factor in therapeutic strategies: The clinical management of early stages of CC includes surgery and/or radiotherapy, whereas radiotherapy and/or concurrent chemotherapy are the recommended therapeutic strategies for locally advanced CC. The histopathological characteristics of tumors can effectively serve as prognostic markers of radiotherapy response; however, the efficacy rate of radiotherapy may significantly differ among cancer patients. Failure of radiotherapy is commonly associated with a higher risk of recurrence, persistence and metastasis; therefore, radioresistance remains the most important and unresolved clinical problem. This condition highlights the importance of precision medicine in searching for possible predictive biomarkers to timely identify patients at risk of treatment response failure and provide tailored therapeutic strategies according to genetic and epigenetic characteristics. The present review aimed to summarize the evidence that supports the role of several proteins, methylation markers and non‑coding RNAs as potential predictive biomarkers for CC.
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Affiliation(s)
- Marcela Lizano
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Universidad Nacional Autónoma de México, Mexico City 14080, Mexico
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Adela Carrillo-García
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Universidad Nacional Autónoma de México, Mexico City 14080, Mexico
| | - Erick De La Cruz-Hernández
- Laboratorio de Investigación en Enfermedades Metabólicas e Infecciosas, División Académica Multidisciplinaria de Comalcalco, Universidad Juárez Autónoma de Tabasco, Ranchería Sur Cuarta Sección, Comalcalco City, Tabasco 86650, Mexico
| | | | - Adriana Contreras-Paredes
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología, Universidad Nacional Autónoma de México, Mexico City 14080, Mexico
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Zhang J, Yao H, Lai C, Sun X, Yang X, Li S, Guo Y, Luo J, Wen Z, Tang K. A novel multimodal prediction model based on DNA methylation biomarkers and low-dose computed tomography images for identifying early-stage lung cancer. Chin J Cancer Res 2023; 35:511-525. [PMID: 37969955 PMCID: PMC10643339 DOI: 10.21147/j.issn.1000-9604.2023.05.08] [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: 08/03/2023] [Accepted: 10/17/2023] [Indexed: 11/17/2023] Open
Abstract
Objective DNA methylation alterations are early events in carcinogenesis and immune signalling in lung cancer. This study aimed to develop a model based on short stature homeobox 2 gene (SHOX2)/prostaglandin E receptor 4 gene (PTGER4) DNA methylation in plasma, appearance subtype of pulmonary nodules (PNs) and low-dose computed tomography (LDCT) images to distinguish early-stage lung cancers. Methods We developed a multimodal prediction model with a training set of 257 individuals. The performance of the multimodal prediction model was further validated in an independent validation set of 42 subjects. In addition, we explored the association between SHOX2/PTGER4 DNA methylation and driver gene mutations in lung cancer based on data from The Cancer Genome Atlas (TCGA) portal. Results There were significant differences between the early-stage lung cancers and benign groups in the methylation levels. The area under a receiver operator characteristic curve (AUC) of SHOX2 in patients with solid nodules, mixed ground-glass opacity nodules and pure ground-glass opacity nodules were 0.693, 0.497 and 0.864, respectively, while the AUCs of PTGER4 were 0.559, 0.739 and 0.619, respectively. With the highest AUC of 0.894, the novel multimodal prediction model outperformed the Mayo Clinic model (0.519) and LDCT-based deep learning model (0.842) in the independent validation set. Database analysis demonstrated that patients with SHOX2/PTGER4 DNA hypermethylation were enriched in TP53 mutations. Conclusions The present multimodal prediction model could more efficiently distinguish early-stage lung cancer from benign PNs. A prognostic index based on DNA methylation and lung cancer driver gene alterations may separate the patients into groups with good or poor prognosis.
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Affiliation(s)
- Jing Zhang
- Division of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Haohua Yao
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Chunliu Lai
- Division of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Xue Sun
- Department of Respiratory and Critical Care Medicine, the Fourth People’s Hospital of Shenyang, Shenyang 110031, China
| | - Xiujuan Yang
- Department of Pharmacy, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Shurong Li
- Department of Radiology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Yubiao Guo
- Division of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Junhang Luo
- Department of Urology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Zhihua Wen
- Department of Radiology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Kejing Tang
- Division of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
- Department of Pharmacy, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
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Stella GM, Lettieri S, Piloni D, Ferrarotti I, Perrotta F, Corsico AG, Bortolotto C. Smart Sensors and Microtechnologies in the Precision Medicine Approach against Lung Cancer. Pharmaceuticals (Basel) 2023; 16:1042. [PMID: 37513953 PMCID: PMC10385174 DOI: 10.3390/ph16071042] [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: 06/08/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND AND RATIONALE The therapeutic interventions against lung cancer are currently based on a fully personalized approach to the disease with considerable improvement of patients' outcome. Alongside continuous scientific progresses and research investments, massive technologic efforts, innovative challenges, and consolidated achievements together with research investments are at the bases of the engineering and manufacturing revolution that allows a significant gain in clinical setting. AIM AND METHODS The scope of this review is thus to focus, rather than on the biologic traits, on the analysis of the precision sensors and novel generation materials, as semiconductors, which are below the clinical development of personalized diagnosis and treatment. In this perspective, a careful revision and analysis of the state of the art of the literature and experimental knowledge is presented. RESULTS Novel materials are being used in the development of personalized diagnosis and treatment for lung cancer. Among them, semiconductors are used to analyze volatile cancer compounds and allow early disease diagnosis. Moreover, they can be used to generate MEMS which have found an application in advanced imaging techniques as well as in drug delivery devices. CONCLUSIONS Overall, these issues represent critical issues only partially known and generally underestimated by the clinical community. These novel micro-technology-based biosensing devices, based on the use of molecules at atomic concentrations, are crucial for clinical innovation since they have allowed the recent significant advances in cancer biology deciphering as well as in disease detection and therapy. There is an urgent need to create a stronger dialogue between technologists, basic researchers, and clinicians to address all scientific and manufacturing efforts towards a real improvement in patients' outcome. Here, great attention is focused on their application against lung cancer, from their exploitations in translational research to their application in diagnosis and treatment development, to ensure early diagnosis and better clinical outcomes.
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Affiliation(s)
- Giulia Maria Stella
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Sara Lettieri
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Davide Piloni
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Ilaria Ferrarotti
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Fabio Perrotta
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", 80131 Napoli, Italy
- U.O.C. Clinica Pneumologica "L. Vanvitelli", A.O. dei Colli, Ospedale Monaldi, 80131 Napoli, Italy
| | - Angelo Guido Corsico
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Chandra Bortolotto
- Department of Clinical-Surgical, Diagnostic and Paediatric Sciences, University of Pavia Medical School, 27100 Pavia, Italy
- Department of Diagnostic Services and Imaging, Unit of Radiology, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
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Tahvilian S, Kuban JD, Yankelevitz DF, Leventon D, Henschke CI, Zhu J, Baden L, Yip R, Hirsch FR, Reed R, Brown A, Muldoon A, Trejo M, Katchman BA, Donovan MJ, Pagano PC. The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules. BMC Pulm Med 2023; 23:193. [PMID: 37277788 PMCID: PMC10240808 DOI: 10.1186/s12890-023-02433-4] [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: 02/07/2023] [Accepted: 04/13/2023] [Indexed: 06/07/2023] Open
Abstract
PURPOSE Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis. METHODS LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated. RESULTS Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52). CONCLUSION Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.
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Affiliation(s)
- Shahram Tahvilian
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Joshua D Kuban
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David F Yankelevitz
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel Leventon
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Claudia I Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey Zhu
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lara Baden
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Rowena Yip
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fred R Hirsch
- Icahn School of Medicine, Center for Thoracic Oncology, Tisch Cancer Institute at Mount Sinai, New York, NY, USA
| | - Rebecca Reed
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Ashley Brown
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Allison Muldoon
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Michael Trejo
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Benjamin A Katchman
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
| | - Michael J Donovan
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA
- Department of Pathology, Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul C Pagano
- LungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
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Voigt W, Prosch H, Silva M. Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges? Cancers (Basel) 2023; 15:cancers15041218. [PMID: 36831559 PMCID: PMC9954060 DOI: 10.3390/cancers15041218] [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: 11/13/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.
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Affiliation(s)
- Wieland Voigt
- Medical Innovation and Management, Steinbeis University Berlin, Ernst-Augustin-Strasse 15, 12489 Berlin, Germany
- Correspondence:
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, General Hospital, 1090 Vienna, Austria
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
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Evaluating Histological Subtypes Classification of Primary Lung Cancers on Unenhanced Computed Tomography Based on Random Forest Model. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:8964676. [PMID: 36794098 PMCID: PMC9925238 DOI: 10.1155/2023/8964676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/07/2022] [Accepted: 01/21/2023] [Indexed: 02/08/2023]
Abstract
Lung cancer is the leading cause of cancer-related death in many countries, and an accurate histopathological diagnosis is of great importance in subsequent treatment. The aim of this study was to establish the random forest (RF) model based on radiomic features to automatically classify and predict lung adenocarcinoma (ADC), lung squamous cell carcinoma (SCC), and small cell lung cancer (SCLC) on unenhanced computed tomography (CT) images. Eight hundred and fifty-two patients (mean age: 61.4, range: 29-87, male/female: 536/316) with preoperative unenhanced CT and postoperative histopathologically confirmed primary lung cancers, including 525 patients with ADC, 161 patients with SCC, and 166 patients with SCLC, were included in this retrospective study. Radiomic features were extracted, selected, and then used to establish the RF classification model to analyse and classify primary lung cancers into three subtypes, including ADC, SCC, and SCLC according to histopathological results. The training (446 ADC, 137 SCC, and 141 SCLC) and testing cohorts (79 ADC, 24 SCC, and 25 SCLC) accounted for 85% and 15% of the whole datasets, respectively. The prediction performance of the RF classification model was evaluated by F1 scores and the receiver operating characteristic (ROC) curve. On the testing cohort, the areas under the ROC curve (AUC) of the RF model in classifying ADC, SCC, and SCLC were 0.74, 0.77, and 0.88, respectively. The F1 scores achieved 0.80, 0.40, and 0.73 in ADC, SCC, and SCLC, respectively, and the weighted average F1 score was 0.71. In addition, for the RF classification model, the precisions were 0.72, 0.64, and 0.70; the recalls were 0.86, 0.29, and 0.76; and the specificities were 0.55, 0.96, and 0.92 in ADC, SCC, and SCLC. The primary lung cancers were feasibly and effectively classified into ADC, SCC, and SCLC based on the combination of RF classification model and radiomic features, which has the potential for noninvasive predicting histological subtypes of primary lung cancers.
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Shen C, Wu Q, Xia Q, Cao C, Wang F, Li Z, Fan L. Establishment of a malignancy and benignancy prediction model of sub-centimeter pulmonary ground-glass nodules based on the inflammation-cancer transformation theory. Front Med (Lausanne) 2022; 9:1007589. [PMID: 36275807 PMCID: PMC9581285 DOI: 10.3389/fmed.2022.1007589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/20/2022] [Indexed: 12/04/2022] Open
Abstract
Background In recent years, Chinese clinicians are frequently encountered by patients with multiple lung nodules and these intensity ground-glass nodules (GGNs) are usually small in size and some of them have no spicule sign. In addition, early lung cancer is diagnosed in large numbers of non-heavy smokers and individuals with no caner history. Obviously, the Mayo model is not applicable to these patients. The aim of the present study is to develop a new and more applicable model that can predict malignancy or benignancy of pulmonary GGNs based on the inflammation-cancer transformation theory. Materials and methods Included in this study were patients who underwent surgical resection or lung puncture biopsy of GGNs in Shanghai 10th People’s Hospital between January 1, 2018 and May 31, 2021 with the inclusion criterion of the maximum diameter of GGN < 1.0 cm. All the included patients had their pulmonary GGNs diagnosed by postoperative pathology. The patient data were analyzed to establish a prediction model and the predictive value of the model was verified. Results Altogether 100 GGN patients who met the inclusion criteria were included for analysis. Based on the results of logistic stepwise regression analysis, a mathematical predication equation was established to calculate the malignancy probability as follows: Malignancy probability rate (p) = ex/(1 + ex); p > 0.5 was considered as malignant and p ≤ 0.5 as benign, where x = 0.9650 + [0.1791 × T helper (Th) cell] + [0.2921 × mixed GGN (mGGN)] + (0.4909 × vascular convergence sign) + (0.1058 × chronic inflammation). According to this prediction model, the positive prediction rate was 73.3% and the negative prediction rate was 100% versus the positive prediction rate of 0% for the Mayo model. Conclusion By focusing on four major factors (chronic inflammation history, human Th cell, imaging vascular convergence sign and mGGNs), the present prediction model greatly improves the accuracy of malignancy or benignancy prediction of sub-centimeter pulmonary GGNs. This is a breakthrough innovation in this field.
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Affiliation(s)
- Changxing Shen
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qiong Wu
- Liangcheng Xincun Community Health Services Center, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qing Xia
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chuanwu Cao
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fei Wang
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhuang Li
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lihong Fan
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China,*Correspondence: Lihong Fan,
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Papalampidou A, Papoutsi E, Katsaounou P. Pulmonary nodule malignancy probability: a diagnostic accuracy meta-analysis of the Mayo model. Clin Radiol 2022; 77:443-450. [DOI: 10.1016/j.crad.2022.01.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Frattini M, Froesch P, Epistolio S. Overview of recent advances in molecular analysis for diagnosing early stage lung cancer nodules. Transl Lung Cancer Res 2022; 10:4303-4307. [PMID: 35004258 PMCID: PMC8674592 DOI: 10.21037/tlcr-21-802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/10/2022]
Affiliation(s)
- Milo Frattini
- Laboratory of Molecular Pathology, Institute of Pathology (ICP), Cantonal Hospital (EOC), Locarno, Switzerland
| | - Patrizia Froesch
- Oncology Institute of Southern Switzerland (IOSI), Cantonal Hospital (EOC), Bellinzona, Switzerland
| | - Samantha Epistolio
- Laboratory of Molecular Pathology, Institute of Pathology (ICP), Cantonal Hospital (EOC), Locarno, Switzerland
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