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Mark P, Papalia I, Lai JK, Pascoe DM. Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience. J Med Imaging Radiat Oncol 2024. [PMID: 39123308 DOI: 10.1111/1754-9485.13734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 07/08/2024] [Indexed: 08/12/2024]
Abstract
INTRODUCTION Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolutional neural networks (CNN); the software of AI-Rad Companion™ (AIRC), capable of self-optimising feature recognition. This study aims to demonstrate the practical value of AI-based lung nodule detection in a clinical setting; a limited body of research. METHODS One hundred and eighty-three non-contrast CT chest studies from a single centre were assessed for AIRC software analysis. Prospectively collected data from AIRC detection and characterisation of lung nodules (size: ≥3 mm) were assessed against the reference standard; reported findings of a blinded consultant radiologist. RESULTS One hundred and sixty-seven CT chest studies were included; 52% indicated for nodule or lung cancer surveillance. Of 289 lung nodules, 219 (75.8%) nodules (mean size: 10.1 mm) were detected by both modalities, 28 (9.7%) were detected by AIRC alone and 42 (14.5%) by radiologist alone. Solid nodules missed by AIRC were larger than those missed by radiologist (11.5 mm vs 4.7 mm, P < 0.001). AIRC software sensitivity was 87.3%, with significant false positive and negative rates demonstrating 12.5% specificity (PPV 0.6, NPV 0.4). CONCLUSION In a population of high nodule prevalence, AIRC lung nodule detection software demonstrates sensitivity comparable to that of consultant radiologist. The clinical significance of larger sized nodules missed by AIRC software presents a barrier to current integration in practice. We consider this research highly relevant in providing focus for ongoing software development, potentiating the future success of AI-based tools within diagnostic radiology.
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Affiliation(s)
- Peter Mark
- Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Isabella Papalia
- Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Jeffrey Kc Lai
- Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Diane M Pascoe
- Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
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Cè M, Ibba S, Cellina M, Tancredi C, Fantesini A, Fazzini D, Fortunati A, Perazzo C, Presta R, Montanari R, Forzenigo L, Carrafiello G, Papa S, Alì M. Radiologists' perceptions on AI integration: An in-depth survey study. Eur J Radiol 2024; 177:111590. [PMID: 38959557 DOI: 10.1016/j.ejrad.2024.111590] [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: 03/15/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. METHODS A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. RESULTS The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. CONCLUSION Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Chiara Tancredi
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | | | - Deborah Fazzini
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Alice Fortunati
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Chiara Perazzo
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Roberta Presta
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Laura Forzenigo
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging SpA, Via Caduti di Marcinelle, 20134 Milan, Italy.
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Gharaibeh NY, De Fazio R, Al-Naami B, Al-Hinnawi AR, Visconti P. Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images. J Imaging 2024; 10:168. [PMID: 39057739 PMCID: PMC11277772 DOI: 10.3390/jimaging10070168] [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: 04/27/2024] [Revised: 06/12/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.
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Affiliation(s)
- Nasr Y. Gharaibeh
- Department of Electrical Engineering, Al-Balqa Applied University, Salt 21163, Jordan;
| | - Roberto De Fazio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
| | - Bassam Al-Naami
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
| | - Abdel-Razzak Al-Hinnawi
- Department of Medical Imaging, Faculty of Allied Medical Sciences, Isra University, Amman 11622, Jordan;
| | - Paolo Visconti
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
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Liu X, Li H, Wang S, Yang S, Zhang G, Xu Y, Yang H, Shan F. CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study. Front Oncol 2024; 14:1420213. [PMID: 38952551 PMCID: PMC11215045 DOI: 10.3389/fonc.2024.1420213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/03/2024] [Indexed: 07/03/2024] Open
Abstract
Purpose To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making. Methods A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor. Results In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002). Conclusion The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.
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Affiliation(s)
- Xiaoyu Liu
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Hongjian Li
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shan Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Guobin Zhang
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yonghua Xu
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital of Fudan University, Fudan University, Shanghai, China
| | - Hanfeng Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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Overkamp F. [A look into the neighboring discipline: eHealth in oncology]. CHIRURGIE (HEIDELBERG, GERMANY) 2024; 95:451-458. [PMID: 38727743 DOI: 10.1007/s00104-024-02089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/09/2024] [Indexed: 05/16/2024]
Abstract
Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.
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Affiliation(s)
- Friedrich Overkamp
- OncoConsult Overkamp GmbH, Europaplatz 2, 10557, Berlin, Deutschland.
- onkowissen.de GmbH, Würzburg, Deutschland.
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Sourlos N, Pelgrim G, Wisselink HJ, Yang X, de Jonge G, Rook M, Prokop M, Sidorenkov G, van Tuinen M, Vliegenthart R, van Ooijen PMA. Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT. Eur Radiol Exp 2024; 8:63. [PMID: 38764066 PMCID: PMC11102890 DOI: 10.1186/s41747-024-00459-9] [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: 12/01/2023] [Accepted: 03/18/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). METHODS Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. RESULTS Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). CONCLUSIONS AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. RELEVANCE STATEMENT In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. KEY POINTS • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.
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Affiliation(s)
- Nikos Sourlos
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - GertJan Pelgrim
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- Department of Oral Surgery of the Medical Spectrum Twente (MST), Enschede, 7500KA, The Netherlands
| | - Hendrik Joost Wisselink
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- DataScience Center in Health (DASH), University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Xiaofei Yang
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Gonda de Jonge
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - Mieneke Rook
- Department of Radiology, Martini Hospital, Groningen, 9728NT, The Netherlands
| | - Mathias Prokop
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - Grigory Sidorenkov
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Marcel van Tuinen
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- DataScience Center in Health (DASH), University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Peter M A van Ooijen
- DataScience Center in Health (DASH), University Medical Center Groningen, Groningen, 9713GZ, The Netherlands.
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, 9713GZ, The Netherlands.
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D'hondt L, Kellens PJ, Torfs K, Bosmans H, Bacher K, Snoeckx A. Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging. Phys Med 2024; 121:103344. [PMID: 38593627 DOI: 10.1016/j.ejmp.2024.103344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/20/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE To validate the performance of computer-aided detection (CAD) and volumetry software using an anthropomorphic phantom with a ground truth (GT) set of 3D-printed nodules. METHODS The Kyoto Kaguku Lungman phantom, containing 3D-printed solid nodules including six diameters (4 to 9 mm) and three morphologies (smooth, lobulated, spiculated), was scanned at varying CTDIvol levels (6.04, 1.54 and 0.20 mGy). Combinations of reconstruction algorithms (iterative and deep learning image reconstruction) and kernels (soft and hard) were applied. Detection, volumetry and density results recorded by a commercially available AI-based algorithm (AVIEW LCS + ) were compared to the absolute GT, which was determined through µCT scanning at 50 µm resolution. The associations between image acquisition parameters or nodule characteristics and accuracy of nodule detection and characterization were analyzed with chi square tests and multiple linear regression. RESULTS High levels of detection sensitivity and precision (minimal 83 % and 91 % respectively) were observed across all acquisitions. Neither reconstruction algorithm nor radiation dose showed significant associations with detection. Nodule diameter however showed a highly significant association with detection (p < 0.0001). Volumetric measurements for nodules > 6 mm were accurate within 10 % absolute range from volumeGT, regardless of dose and reconstruction. Nodule diameter and morphology are major determinants of volumetric accuracy (p < 0.001). Density assignment was not significantly influenced by any parameters. CONCLUSIONS Our study confirms the software's accurate performance in nodule volumetry, detection and density characterization with robustness for variations in CT imaging protocols. This study suggests the incorporation of similar phantom setups in quality assurance of CAD tools.
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Affiliation(s)
- Louise D'hondt
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Proeftuinstraat 86, Ghent, Belgium; Faculty of Medicine, University of Antwerp, Universiteitsplein 1, Wilrijk, Belgium.
| | - Pieter-Jan Kellens
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Proeftuinstraat 86, Ghent, Belgium
| | - Kwinten Torfs
- Leuven University Center of Medical Physics in Radiology, University Hospitals Leuven, Herestraat 49, Leuven, Belgium
| | - Hilde Bosmans
- Leuven University Center of Medical Physics in Radiology, University Hospitals Leuven, Herestraat 49, Leuven, Belgium
| | - Klaus Bacher
- Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Proeftuinstraat 86, Ghent, Belgium
| | - Annemiek Snoeckx
- Faculty of Medicine, University of Antwerp, Universiteitsplein 1, Wilrijk, Belgium; Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
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Yamada D, Kojima F, Otsuka Y, Kawakami K, Koishi N, Oba K, Bando T, Matsusako M, Kurihara Y. Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study. BMJ Open Respir Res 2024; 11:e002249. [PMID: 38589197 PMCID: PMC11015206 DOI: 10.1136/bmjresp-2023-002249] [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: 12/12/2023] [Accepted: 03/22/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Diagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical planning. This indicates the need for a more efficient and patient-centred approach, suggesting a gap in the existing diagnostic methods and the potential for artificial intelligence technologies to address this gap. This study aimed to create a multimodal hybrid transformer model using the Vision Transformer that leverages LDCT features and clinical data to improve surgical decision-making for patients with incidentally detected mediastinal tumours. METHODS This retrospective study analysed patients with mediastinal tumours between 2010 and 2021. Patients eligible for surgery (n=30) were considered 'positive,' whereas those without tumour enlargement (n=32) were considered 'negative.' We developed a hybrid model combining a convolutional neural network with a transformer to integrate imaging and clinical data. The dataset was split in a 5:3:2 ratio for training, validation and testing. The model's efficacy was evaluated using a receiver operating characteristic (ROC) analysis across 25 iterations of random assignments and compared against conventional radiomics models and models excluding clinical data. RESULTS The multimodal hybrid model demonstrated a mean area under the curve (AUC) of 0.90, significantly outperforming the non-clinical data model (AUC=0.86, p=0.04) and radiomics models (random forest AUC=0.81, p=0.008; logistic regression AUC=0.77, p=0.004). CONCLUSION Integrating clinical and LDCT data using a hybrid transformer model can improve surgical decision-making for mediastinal tumours, showing superiority over models lacking clinical data integration.
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Affiliation(s)
- Daisuke Yamada
- Department of Radiology, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Fumitsugu Kojima
- Department of Thoracic Surgery, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Yujiro Otsuka
- Department of Radiology, Juntendo University, Bunkyo-ku, Japan
- Plusman LLC, Tokyo, Japan
| | - Kouhei Kawakami
- Department of Radiology, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Naoki Koishi
- Department of Radiology, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Ken Oba
- Department of Radiology, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Toru Bando
- Department of Thoracic Surgery, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Masaki Matsusako
- Department of Radiology, Saint Luke's International Hospital, Chuo-ku, Japan
| | - Yasuyuki Kurihara
- Department of Radiology, Saint Luke's International Hospital, Chuo-ku, Japan
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Quanyang W, Yao H, Sicong W, Linlin Q, Zewei Z, Donghui H, Hongjia L, Shijun Z. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Cancer Med 2024; 13:e7140. [PMID: 38581113 PMCID: PMC10997848 DOI: 10.1002/cam4.7140] [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: 11/24/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. RESULTS AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. CONCLUSIONS AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.
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Affiliation(s)
- Wu Quanyang
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huang Yao
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wang Sicong
- Magnetic Resonance Imaging ResearchGeneral Electric Healthcare (China)BeijingChina
| | - Qi Linlin
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Zewei
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hou Donghui
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Hongjia
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhao Shijun
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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10
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Winkelmann MT, Gassenmaier S, Walter SS, Artzner C, Nikolaou K, Bongers MN. Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography. Tomography 2024; 10:255-265. [PMID: 38393288 PMCID: PMC10892507 DOI: 10.3390/tomography10020020] [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: 12/29/2023] [Revised: 01/30/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: -20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.
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Affiliation(s)
- Moritz T. Winkelmann
- Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany; (S.G.); (S.S.W.); (C.A.); (K.N.); (M.N.B.)
| | - Sebastian Gassenmaier
- Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany; (S.G.); (S.S.W.); (C.A.); (K.N.); (M.N.B.)
| | - Sven S. Walter
- Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany; (S.G.); (S.S.W.); (C.A.); (K.N.); (M.N.B.)
| | - Christoph Artzner
- Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany; (S.G.); (S.S.W.); (C.A.); (K.N.); (M.N.B.)
- Institute of Radiology: Diakonie Klinikum Stuttgart, 70174 Stuttgart, Germany
| | - Konstantin Nikolaou
- Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany; (S.G.); (S.S.W.); (C.A.); (K.N.); (M.N.B.)
| | - Malte N. Bongers
- Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany; (S.G.); (S.S.W.); (C.A.); (K.N.); (M.N.B.)
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11
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Cè M, Oliva G, Rabaiotti FL, Macrì L, Zollo S, Aquila A, Cellina M. Portable Dynamic Chest Radiography: Literature Review and Potential Bedside Applications. Med Sci (Basel) 2024; 12:10. [PMID: 38390860 PMCID: PMC10885043 DOI: 10.3390/medsci12010010] [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: 01/06/2024] [Revised: 02/01/2024] [Accepted: 02/03/2024] [Indexed: 02/24/2024] Open
Abstract
Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient's bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient's bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy; (F.L.R.); (L.M.); (A.A.)
| | - Giancarlo Oliva
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy; (G.O.); (M.C.)
| | - Francesca Lucrezia Rabaiotti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy; (F.L.R.); (L.M.); (A.A.)
| | - Laura Macrì
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy; (F.L.R.); (L.M.); (A.A.)
| | - Sharon Zollo
- Konica Minolta Business Solutions Europe GmbH, Capellalaan 65, 2132 JL Hoofddorp, The Netherlands;
| | - Alessandro Aquila
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy; (F.L.R.); (L.M.); (A.A.)
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy; (G.O.); (M.C.)
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12
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Yoon SH, Oh DY, Kim HJ, Jang S, Kim M, Kim J, Lee KW, Lee KJ, Kim J. Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans. Quant Imaging Med Surg 2024; 14:1493-1506. [PMID: 38415154 PMCID: PMC10895128 DOI: 10.21037/qims-23-1174] [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: 08/17/2023] [Accepted: 11/29/2023] [Indexed: 02/29/2024]
Abstract
Background Detecting new pulmonary metastases by comparing serial computed tomography (CT) scans is crucial, but a repetitive and time-consuming task that burdens the radiologists' workload. This study aimed to evaluate the usefulness of a nodule-matching algorithm with deep learning-based computer-aided detection (DL-CAD) in diagnosing new pulmonary metastases on cancer surveillance CT scans. Methods Among patients who underwent pulmonary metastasectomy between 2014 and 2018, 65 new pulmonary metastases missed by interpreting radiologists on cancer surveillance CT (Time 2) were identified after a retrospective comparison with the previous CT (Time 1). First, DL-CAD detected nodules in Time 1 and Time 2 CT images. All nodules detected at Time 2 were initially considered metastasis candidates. Second, the nodule-matching algorithm was used to assess the correlation between the nodules from the two CT scans and to classify the nodules at Time 2 as "new" or "pre-existing". Pre-existing nodules were excluded from metastasis candidates. We evaluated the performance of DL-CAD with the nodule-matching algorithm, based on its sensitivity, false-metastasis candidates per scan, and positive predictive value (PPV). Results A total of 475 lesions were detected by DL-CAD at Time 2. Following a radiologist review, the lesions were categorized as metastases (n=54), benign nodules (n=392), and non-nodules (n=29). Upon comparison of nodules at Time 1 and 2 using the nodule-matching algorithm, all metastases were classified as new nodules without any matching errors. Out of 421 benign lesions, 202 (48.0%) were identified as pre-existing and subsequently excluded from the pool of metastasis candidates through the nodule-matching algorithm. As a result, false-metastasis candidates per CT scan decreased by 47.9% (from 7.1 to 3.7, P<0.001) and the PPV increased from 11.4% to 19.8% (P<0.001), while maintaining sensitivity. Conclusions The nodule-matching algorithm improves the diagnostic performance of DL-CAD for new pulmonary metastases, by lowering the number of false-metastasis candidates without compromising sensitivity.
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Affiliation(s)
- Sung Hyun Yoon
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Dong Yul Oh
- Monitor Corporation, Seoul, Republic of Korea
| | - Hyo Jin Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sowon Jang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Minseon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jihang Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Kyung Won Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Kyong Joon Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Monitor Corporation, Seoul, Republic of Korea
| | - Junghoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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13
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Abdelrahman K, Shiyovich A, Huck DM, Berman AN, Weber B, Gupta S, Cardoso R, Blankstein R. Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification. Diagnostics (Basel) 2024; 14:125. [PMID: 38248002 PMCID: PMC10814920 DOI: 10.3390/diagnostics14020125] [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: 11/08/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.
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Affiliation(s)
| | | | | | | | | | | | | | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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14
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Shah IA, Mishra S. Artificial intelligence in advancing occupational health and safety: an encapsulation of developments. J Occup Health 2024; 66:uiad017. [PMID: 38334203 PMCID: PMC10878366 DOI: 10.1093/joccuh/uiad017] [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: 10/26/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 02/10/2024] Open
Abstract
OBJECTIVES In an era characterized by dynamic technological advancements, the well-being of the workforce remains a cornerstone of progress and sustainability. The evolving industrial landscape in the modern world has had a considerable influence on occupational health and safety (OHS). Ensuring the well-being of workers and creating safe working environments are not only ethical imperatives but also integral to maintaining operational efficiency and productivity. We aim to review the advancements that have taken place with a potential to reshape workplace safety with integration of artificial intelligence (AI)-driven new technologies to prevent occupational diseases and promote safety solutions. METHODS The published literature was identified using scientific databases of Embase, PubMed, and Google scholar including a lower time bound of 1974 to capture chronological advances in occupational disease detection and technological solutions employed in industrial set-ups. RESULTS AI-driven technologies are revolutionizing how organizations approach health and safety, offering predictive insights, real-time monitoring, and risk mitigation strategies that not only minimize accidents and hazards but also pave the way for a more proactive and responsive approach to safeguarding the workforce. CONCLUSION As industries embrace the transformative potential of AI, a new frontier of possibilities emerges for enhancing workplace safety. This synergy between OHS and AI marks a pivotal moment in the quest for safer, healthier, and more sustainable workplaces.
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Affiliation(s)
- Immad A Shah
- Division of Health Sciences, ICMR-National Institute of Occupational Health, Ahmedabad, Gujarat, India
| | - SukhDev Mishra
- Department of Biostatistics, Division of Health Sciences, ICMR-National Institute of Occupational Health, Ahmedabad, Gujarat, India
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15
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Alì M, Fantesini A, Morcella MT, Ibba S, D'Anna G, Fazzini D, Papa S. Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges. Crit Rev Oncog 2024; 29:29-35. [PMID: 38505879 DOI: 10.1615/critrevoncog.2023050584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.
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Affiliation(s)
- Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Arianna Fantesini
- Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy
| | | | - Simona Ibba
- CDI Centro Diagnostico Italiano, Via Saint Bon 20, Milan, Italy
| | - Gennaro D'Anna
- Neuroimaging Unit, ASST Ovest Milanese, Via Papa Giovanni Paolo II, Legnano (Milan), Italy
| | - Deborah Fazzini
- CDI Centro Diagnostico Italiano, Via Saint Bon 20, Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
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16
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Cellina M, De Padova G, Caldarelli N, Libri D, Cè M, Martinenghi C, Alì M, Papa S, Carrafiello G. Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy. Crit Rev Oncog 2024; 29:1-13. [PMID: 38505877 DOI: 10.1615/critrevoncog.2023050439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giuseppe De Padova
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Nazarena Caldarelli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Dario Libri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, Ospedale San Raffaele, Via Olgettina, 60 - 20132 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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17
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Triggiani S, Contaldo MT, Mastellone G, Cè M, Ierardi AM, Carrafiello G, Cellina M. The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review. Crit Rev Oncog 2024; 29:37-52. [PMID: 38505880 DOI: 10.1615/critrevoncog.2023049855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.
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Affiliation(s)
- Sonia Triggiani
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maria T Contaldo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Giulia Mastellone
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Anna M Ierardi
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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18
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Cè M, Cellina M. Preface: Artificial Intelligence and the Revolution of Oncological Imaging. Crit Rev Oncog 2024; 29:ix-xi. [PMID: 38505876 DOI: 10.1615/critrevoncog.v29.i2.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Affiliation(s)
- Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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19
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Cellina M, Irmici G, Pepa GD, Ce M, Chiarpenello V, Alì M, Papa S, Carrafiello G. Radiomics and Artificial Intelligence in Renal Lesion Assessment. Crit Rev Oncog 2024; 29:65-75. [PMID: 38505882 DOI: 10.1615/critrevoncog.2023051084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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20
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Irmici G, Cè M, Pepa GD, D'Ascoli E, De Berardinis C, Giambersio E, Rabiolo L, La Rocca L, Carriero S, Depretto C, Scaperrotta G, Cellina M. Exploring the Potential of Artificial Intelligence in Breast Ultrasound. Crit Rev Oncog 2024; 29:15-28. [PMID: 38505878 DOI: 10.1615/critrevoncog.2023048873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient's care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.
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Affiliation(s)
- Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elisa D'Ascoli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Claudia De Berardinis
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Emilia Giambersio
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Lidia Rabiolo
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Policlinico Università di Palermo, Palermo, Italy
| | - Ludovica La Rocca
- Postgraduation School in Radiodiagnostics, Università degli Studi di Napoli
| | - Serena Carriero
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Catherine Depretto
- Breast Radiology Unit, Fondazione IRCCS, Istituto Nazionale Tumori, Milano, Italy
| | | | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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21
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Alshmrani GM, Ni Q, Jiang R, Muhammed N. Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans. Diagnostics (Basel) 2023; 13:3481. [PMID: 37998617 PMCID: PMC10670323 DOI: 10.3390/diagnostics13223481] [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: 10/06/2023] [Revised: 11/09/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023] Open
Abstract
The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.
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Affiliation(s)
- Goram Mufarah Alshmrani
- School of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UK; (Q.N.); (R.J.)
- College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia
| | - Qiang Ni
- School of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UK; (Q.N.); (R.J.)
| | - Richard Jiang
- School of Computing and Commutations, Lancaster University, Lancaster LA1 4YW, UK; (Q.N.); (R.J.)
| | - Nada Muhammed
- Computers and Control Engineering Department, Faculty of Engineering, Tanta University, Tanta 31733, Egypt;
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22
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Faruqui N, Yousuf MA, Kateb FA, Abdul Hamid M, Monowar MM. Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis. Heliyon 2023; 9:e21520. [PMID: 37942151 PMCID: PMC10628703 DOI: 10.1016/j.heliyon.2023.e21520] [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: 05/27/2023] [Revised: 09/27/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023] Open
Abstract
The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability-a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A-Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.
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Affiliation(s)
- Nuruzzaman Faruqui
- Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
- Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Mohammad Abu Yousuf
- Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
| | - Faris A. Kateb
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Md. Abdul Hamid
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Muhammad Mostafa Monowar
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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23
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Gandhi Z, Gurram P, Amgai B, Lekkala SP, Lokhandwala A, Manne S, Mohammed A, Koshiya H, Dewaswala N, Desai R, Bhopalwala H, Ganti S, Surani S. Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes. Cancers (Basel) 2023; 15:5236. [PMID: 37958411 PMCID: PMC10650618 DOI: 10.3390/cancers15215236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
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Affiliation(s)
- Zainab Gandhi
- Department of Internal Medicine, Geisinger Wyoming Valley Medical Center, Wilkes Barre, PA 18711, USA
| | - Priyatham Gurram
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Birendra Amgai
- Department of Internal Medicine, Geisinger Community Medical Center, Scranton, PA 18510, USA;
| | - Sai Prasanna Lekkala
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Alifya Lokhandwala
- Department of Medicine, Jawaharlal Nehru Medical College, Wardha 442001, India;
| | - Suvidha Manne
- Department of Medicine, Mamata Medical College, Khammam 507002, India; (P.G.); (S.P.L.); (S.M.)
| | - Adil Mohammed
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48602, USA;
| | - Hiren Koshiya
- Department of Internal Medicine, Prime West Consortium, Inglewood, CA 92395, USA;
| | - Nakeya Dewaswala
- Department of Cardiology, University of Kentucky, Lexington, KY 40536, USA;
| | - Rupak Desai
- Independent Researcher, Atlanta, GA 30079, USA;
| | - Huzaifa Bhopalwala
- Department of Internal Medicine, Appalachian Regional Hospital, Hazard, KY 41701, USA; (H.B.); (S.G.)
| | - Shyam Ganti
- Department of Internal Medicine, Appalachian Regional Hospital, Hazard, KY 41701, USA; (H.B.); (S.G.)
| | - Salim Surani
- Departmet of Pulmonary, Critical Care Medicine, Texas A&M University, College Station, TX 77845, USA;
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24
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Lu N, Guan X, Zhu J, Li Y, Zhang J. A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation. Cancers (Basel) 2023; 15:4497. [PMID: 37760468 PMCID: PMC10526233 DOI: 10.3390/cancers15184497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE This study aimed to build a deep learning system using enhanced computed tomography (CT) portal-phase images for predicting colorectal cancer patients' preoperative staging and RAS gene mutation status. METHODS The contrast-enhanced CT image dataset comprises the CT portal-phase images from a retrospective cohort of 231 colorectal cancer patients. The deep learning system was developed via migration learning for colorectal cancer detection, staging, and RAS gene mutation status prediction. This study used pre-trained Yolov7, vision transformer (VIT), swin transformer (SWT), EfficientNetV2, and ConvNeXt. 4620, and contrast-enhanced CT images and annotated tumor bounding boxes were included in the tumor identification and staging dataset. A total of 19,700 contrast-enhanced CT images comprise the RAS gene mutation status prediction dataset. RESULTS In the validation cohort, the Yolov7-based detection model detected and staged tumors with a mean accuracy precision (IoU = 0.5) (mAP_0.5) of 0.98. The area under the receiver operating characteristic curve (AUC) in the test set and validation set for the VIT-based prediction model in predicting the mutation status of the RAS genes was 0.9591 and 0.9554, respectively. The detection network and prediction network of the deep learning system demonstrated great performance in explaining contrast-enhanced CT images. CONCLUSION In this study, a deep learning system was created based on the foundation of contrast-enhanced CT portal-phase imaging to preoperatively predict the stage and RAS mutation status of colorectal cancer patients. This system will help clinicians choose the best treatment option to increase colorectal cancer patients' chances of survival and quality of life.
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Affiliation(s)
- Na Lu
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Xiao Guan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
| | - Jianguo Zhu
- Department of Radiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China;
| | - Yuan Li
- Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China;
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, No. 121, Jiangjiayuan Road, Nanjing 210011, China (X.G.)
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25
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Cellina M, Cacioppa LM, Cè M, Chiarpenello V, Costa M, Vincenzo Z, Pais D, Bausano MV, Rossini N, Bruno A, Floridi C. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers (Basel) 2023; 15:4344. [PMID: 37686619 PMCID: PMC10486721 DOI: 10.3390/cancers15174344] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milano, Italy;
| | - Laura Maria Cacioppa
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Marco Costa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Zakaria Vincenzo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Daniele Pais
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Maria Vittoria Bausano
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Nicolò Rossini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Chiara Floridi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Division of Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
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26
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Bianchi LMG, Irmici G, Cè M, D'Ascoli E, Della Pepa G, Di Vita F, Casati O, Soresina M, Menozzi A, Khenkina N, Cellina M. Diagnosis and Treatment of Post-Prostatectomy Lymphedema: What's New? Curr Oncol 2023; 30:4512-4526. [PMID: 37232799 DOI: 10.3390/curroncol30050341] [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: 03/14/2023] [Revised: 04/24/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Lymphedema is a chronic progressive disorder that significantly compromises patients' quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema.
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Affiliation(s)
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Elisa D'Ascoli
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Filippo Di Vita
- Postgraduation School in Plastic Surgery, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Omar Casati
- Postgraduation School in Plastic Surgery, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Massimo Soresina
- Plastic Surgery Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Andrea Menozzi
- Plastic Surgery Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Natallia Khenkina
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
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27
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Irmici G, Cè M, Caloro E, Khenkina N, Della Pepa G, Ascenti V, Martinenghi C, Papa S, Oliva G, Cellina M. Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? Diagnostics (Basel) 2023; 13:diagnostics13020216. [PMID: 36673027 PMCID: PMC9858224 DOI: 10.3390/diagnostics13020216] [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: 12/10/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
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Affiliation(s)
- Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natallia Khenkina
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Giancarlo Oliva
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
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28
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Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L, Pellegrino G, D’Amico N, Papa S, Carrafiello G. Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12123223. [PMID: 36553230 PMCID: PMC9777804 DOI: 10.3390/diagnostics12123223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/11/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
- Correspondence:
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Lorenzo Bianchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giuseppe Pellegrino
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natascha D’Amico
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Via Sforza 35, 20122 Milan, Italy
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