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Grenda A, Krawczyk P, Obara A, Gajek Ł, Łomża-Łaba A, Milanowski J. Transitioning to a Personalized Approach in Molecularly Subtyped Small-Cell Lung Cancer (SCLC). Int J Mol Sci 2024; 25:4208. [PMID: 38673793 PMCID: PMC11050005 DOI: 10.3390/ijms25084208] [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: 02/29/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Lung cancer has become a major public health concern, standing as the leading cause of cancer-related deaths worldwide. Among its subtypes, small-cell lung cancer (SCLC) is characterized by aggressive and rapid growth, poor differentiation, and neuroendocrine features. Typically, SCLC is diagnosed at an advanced stage (extensive disease, ED-SCLC), with distant metastases, and is strongly associated with tobacco smoking and has a poor prognosis. Recent clinical trials, such as CASPIAN and IMpower133, have demonstrated promising outcomes with the incorporation of immune checkpoint inhibitors in first-line chemotherapy, leading to prolonged progression-free survival and overall survival in patients with ED-SCLC compared to standard chemotherapy. Other studies have emphasized the potential for future development of molecularly targeted therapies in SCLC patients, including inhibitors of IGF-1R, DLL3, BCL-2, MYC, or PARP. The molecular subdivision of SCLC based on transcriptomic and immunohistochemical analyses represents a significant advancement in both diagnostic and clinical approaches in SCLC patients. Specific molecular pathways are activated within distinct transcriptome subtypes of SCLC, offering the potential for personalized treatment strategies, such as targeted therapies and immunotherapies. Such tailored approaches hold promise for significantly improving outcomes in SCLC patients.
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Affiliation(s)
- Anna Grenda
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-950 Lublin, Poland; (P.K.); (A.Ł.-Ł.); (J.M.)
| | - Paweł Krawczyk
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-950 Lublin, Poland; (P.K.); (A.Ł.-Ł.); (J.M.)
| | - Adrian Obara
- Institute of Genetics and Immunology Genim LCC, Filaretów 27/2, 20-609 Lublin, Poland; (A.O.); (Ł.G.)
| | - Łukasz Gajek
- Institute of Genetics and Immunology Genim LCC, Filaretów 27/2, 20-609 Lublin, Poland; (A.O.); (Ł.G.)
| | - Aleksandra Łomża-Łaba
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-950 Lublin, Poland; (P.K.); (A.Ł.-Ł.); (J.M.)
| | - Janusz Milanowski
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-950 Lublin, Poland; (P.K.); (A.Ł.-Ł.); (J.M.)
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Tomassini S, Falcionelli N, Bruschi G, Sbrollini A, Marini N, Sernani P, Morettini M, Müller H, Dragoni AF, Burattini L. On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Comput Med Imaging Graph 2023; 110:102310. [PMID: 37979340 DOI: 10.1016/j.compmedimag.2023.102310] [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: 07/18/2023] [Revised: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Nicola Falcionelli
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Paolo Sernani
- Department of Law, University of Macerata (UNIMC), Macerata, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Aldo Franco Dragoni
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy.
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel) 2023; 15:3981. [PMID: 37568797 PMCID: PMC10417369 DOI: 10.3390/cancers15153981] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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Affiliation(s)
- Athena Davri
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
| | - Effrosyni Birbas
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Theofilos Kanavos
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Georgios Ntritsos
- Department of Hygiene and Epidemiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Anna Batistatou
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
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Tomassini S, Falcionelli N, Sernani P, Burattini L, Dragoni AF. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey. Comput Biol Med 2022; 146:105691. [PMID: 35691714 DOI: 10.1016/j.compbiomed.2022.105691] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
Abstract
Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Nicola Falcionelli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Paolo Sernani
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Aldo Franco Dragoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
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