1
|
Wang J, Liu JH, Sun Y, Li P, Gao K, Wang J. Machine learning-based MRI radiomics to predict postoperative complications following peripheral nerve sheath tumour excision. J Hand Surg Eur Vol 2025:17531934251327834. [PMID: 40219856 DOI: 10.1177/17531934251327834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/14/2025]
Abstract
This study sought to establish and validate a machine learning-based multi-sequence MRI radiomics model for predicting postoperative complications in patients with peripheral nerve sheath tumours. We conducted a retrospective analysis of 303 patients with pathologically confirmed tumours, extracting features from T1-weighted and T2-weighted MRI scans. Relevant radiomic features were identified through interclass correlation coefficient analysis, t-tests and least absolute shrinkage and selection operator techniques. A multi-sequence radiomics model was developed using the Light Gradient Boosting Machine classifier, alongside a clinical-radiomics model that incorporated clinical features. The models exhibited robust diagnostic performance, with areas under the receiver operating characteristic curve reaching 0.95 in the training cohort. These findings underscore the model's potential to accurately predict postoperative complications, providing crucial support for clinicians in devising personalized treatment strategies for patients with peripheral nerve sheath tumours.Level of evidence: Prognostic III.
Collapse
Affiliation(s)
- Jifeng Wang
- Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Hand Surgery and Peripheral Nerve Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jia Hao Liu
- Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Hand Surgery and Peripheral Nerve Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yinuo Sun
- Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Hand Surgery and Peripheral Nerve Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Peifeng Li
- Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Hand Surgery and Peripheral Nerve Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiming Gao
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Wang
- Wound Repair Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Hand Surgery and Peripheral Nerve Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| |
Collapse
|
2
|
Li L, Yang W, Jia H. Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis. J Inflamm Res 2025; 18:2943-2953. [PMID: 40026307 PMCID: PMC11872085 DOI: 10.2147/jir.s499512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Accepted: 01/30/2025] [Indexed: 03/05/2025] Open
Abstract
Background and Aim Idiopathic granulomatous mastitis (IGM) is a rare chronic inflammatory breast disease that presents significant challenges in diagnosis and treatment. Predicting the recurrence of IGM is crucial for effective patient management and improved treatment outcomes. This study aims to evaluate and compare the performance of different machine learning models, including logistic regression, random forest, and neural networks, in predicting IGM recurrence using patient data. Methods A retrospective analysis was conducted on 212 patients diagnosed with IGM. Collected data included comprehensive serological markers, tumor characteristics, and treatment history. The dataset was divided into a training set (70%) and a testing set (30%). Data preprocessing involved normalization, feature selection, and data augmentation to ensure model robustness. Three predictive models were developed and compared: logistic regression, random forest, and neural networks. Performance metrics such as accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were used to evaluate each model's ability to predict IGM recurrence. Results The logistic regression model achieved an AUC of 0.837, 0.725 and 0.829 in the training cohort, validation cohort and test cohort. The random forest model showed improved performance with an AUC of 0.797, 0.755 and 0.793 in the training cohort, validation cohort and test cohort. The neural network model outperformed both the logistic regression and random forest models, with an AUC of 0.938, 0.880 and 0.913 and a better F1 score. Feature importance analysis indicated that variables such as smoking, surgery and a history of oral contraceptive use were most important in predicting recurrence. Conclusion This study demonstrates that, compared to logistic regression and random forest models, neural networks have superior performance in predicting the recurrence of granulomatous mastitis. The high accuracy and reliability of the neural network model highlight its potential clinical application in the early and accurate prediction of IGM recurrence.
Collapse
Affiliation(s)
- Lanying Li
- Vascular Surgery Breast Surgery Department, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, 621000, People’s Republic of China
- Clinical Medicine School, North Sichuan Medical College, Nanchong, Sichuan, 637000, People’s Republic of China
| | - Wen Yang
- General Surgery Department, Lanzhou Second People’s Hospital, Lanzhou, Gansu, 730000, People’s Republic of China
| | - Haiming Jia
- Vascular Surgery Breast Surgery Department, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, 621000, People’s Republic of China
| |
Collapse
|
3
|
Bozzo A, Hollingsworth A, Chatterjee S, Apte A, Deng J, Sun S, Tap W, Aoude A, Bhatnagar S, Healey JH. A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma. NPJ Precis Oncol 2024; 8:188. [PMID: 39237726 PMCID: PMC11377835 DOI: 10.1038/s41698-024-00695-7] [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: 10/06/2023] [Accepted: 08/30/2024] [Indexed: 09/07/2024] Open
Abstract
The objective of this study is to develop a multimodal neural network (MMNN) model that analyzes clinical variables and MRI images of a soft tissue sarcoma (STS) patient, to predict overall survival and risk of distant metastases. We compare the performance of this MMNN to models based on clinical variables alone, radiomics models, and an unimodal neural network. We include patients aged 18 or older with biopsy-proven STS who underwent primary resection between January 1st, 2005, and December 31st, 2020 with complete outcome data and a pre-treatment MRI with both a T1 post-contrast sequence and a T2 fat-sat sequence available. A total of 9380 MRI slices containing sarcomas from 287 patients are available. Our MMNN accepts the entire 3D sarcoma volume from T1 and T2 MRIs and clinical variables. Gradient blending allows the clinical and image sub-networks to optimally converge without overfitting. Heat maps were generated to visualize the salient image features. Our MMNN outperformed all other models in predicting overall survival and the risk of distant metastases. The C-Index of our MMNN for overall survival is 0.77 and the C-Index for risk of distant metastases is 0.70. The provided heat maps demonstrate areas of sarcomas deemed most salient for predictions. Our multimodal neural network with gradient blending improves predictions of overall survival and risk of distant metastases in patients with soft tissue sarcoma. Future work enabling accurate subtype-specific predictions will likely utilize similar end-to-end multimodal neural network architecture and require prospective curation of high-quality data, the inclusion of genomic data, and the involvement of multiple centers through federated learning.
Collapse
Affiliation(s)
- Anthony Bozzo
- Orthopaedic Service of the Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Division of Orthopaedic Surgery, McGill University, Montreal, QC, Canada.
| | - Alex Hollingsworth
- AI/ML and NextGen Analytics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Subrata Chatterjee
- AI/ML and NextGen Analytics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Aditya Apte
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jiawen Deng
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Simon Sun
- Musculoskeletal Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William Tap
- Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ahmed Aoude
- Division of Orthopaedic Surgery, McGill University, Montreal, QC, Canada
| | - Sahir Bhatnagar
- Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
| | - John H Healey
- Orthopaedic Service of the Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
4
|
Borghi A, Gronchi A. Extremity and Truncal Soft Tissue Sarcoma: Risk Assessment and Multidisciplinary Management. Semin Radiat Oncol 2024; 34:147-163. [PMID: 38508780 DOI: 10.1016/j.semradonc.2023.12.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Extremity and truncal soft tissue sarcomas are a heterogeneous group of rare cancers that arise from mesenchymal tissues. Hence, the adoption of tailored risk assessment and prognostication tools plays a crucial role in optimizing the decision-making for which of the many possible treatment strategies to select. Management of these tumors requires a multidisciplinary strategy, which has seen significant development in recent decades. Surgery has emerged as the primary treatment approach, with the main goal of achieving microscopic negative tumor margins. To reduce the likelihood of local recurrence, loco-regional treatments such as radiation therapy and isolated limb perfusion are often added to the treatment regimen in combination with surgery. This approach also enables surgeons to perform limb-sparing surgery, particularly in cases where a positive tumor margin is expected. Chemotherapy may also provide a further benefit in decreasing the probability of local recurrence or reducing distant metastasis in selected patients. Selecting the optimal treatment strategy for these rare tumors is best accomplished by an experienced multi-disciplinary team.
Collapse
Affiliation(s)
- Alessandra Borghi
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Alessandro Gronchi
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy..
| |
Collapse
|
5
|
De Angelis R, Casale R, Coquelet N, Ikhlef S, Mokhtari A, Simoni P, Bali MA. The impact of radiomics in the management of soft tissue sarcoma. Discov Oncol 2024; 15:62. [PMID: 38441726 PMCID: PMC10914656 DOI: 10.1007/s12672-024-00908-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/23/2024] [Indexed: 03/08/2024] Open
Abstract
INTRODUCTION Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation. MATERIALS AND METHODS We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: 'radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])'. Only original articles, referring to humans, were included. RESULTS A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response. CONCLUSIONS With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.
Collapse
Affiliation(s)
- Riccardo De Angelis
- Institut Jules Bordet, Anderlecht, Belgium
- Université Libre de Bruxelles, Brussels, Belgium
| | - Roberto Casale
- Institut Jules Bordet, Anderlecht, Belgium.
- Université Libre de Bruxelles, Brussels, Belgium.
| | | | - Samia Ikhlef
- Institut Jules Bordet, Anderlecht, Belgium
- Université Libre de Bruxelles, Brussels, Belgium
| | - Ayoub Mokhtari
- Institut Jules Bordet, Anderlecht, Belgium.
- Université Libre de Bruxelles, Brussels, Belgium.
| | - Paolo Simoni
- Université Libre de Bruxelles, Brussels, Belgium
| | - Maria Antonietta Bali
- Institut Jules Bordet, Anderlecht, Belgium
- Université Libre de Bruxelles, Brussels, Belgium
| |
Collapse
|
6
|
Gitto S, Cuocolo R, Huisman M, Messina C, Albano D, Omoumi P, Kotter E, Maas M, Van Ooijen P, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. Insights Imaging 2024; 15:54. [PMID: 38411750 PMCID: PMC10899555 DOI: 10.1186/s13244-024-01614-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. METHODS A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. RESULTS Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. CONCLUSIONS Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.
Collapse
Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Merel Huisman
- Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen, The Netherlands
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Elmar Kotter
- Department of Radiology, Freiburg University Medical Center, Freiburg, Germany
| | - Mario Maas
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
| |
Collapse
|
7
|
Marcellinaro R, Spoletini D, Grieco M, Avella P, Cappuccio M, Troiano R, Lisi G, Garbarino GM, Carlini M. Colorectal Cancer: Current Updates and Future Perspectives. J Clin Med 2023; 13:40. [PMID: 38202047 PMCID: PMC10780254 DOI: 10.3390/jcm13010040] [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: 10/18/2023] [Revised: 12/12/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
Colorectal cancer is a frequent neoplasm in western countries, mainly due to dietary and behavioral factors. Its incidence is growing in developing countries for the westernization of foods and lifestyles. An increased incidence rate is observed in patients under 45 years of age. In recent years, the mortality for CRC is decreased, but this trend is slowing. The mortality rate is reducing in those countries where prevention and treatments have been implemented. The survival is increased to over 65%. This trend reflects earlier detection of CRC through routine clinical examinations and screening, more accurate staging through advances in imaging, improvements in surgical techniques, and advances in chemotherapy and radiation. The most important predictor of survival is the stage at diagnosis. The screening programs are able to reduce incidence and mortality rates of CRC. The aim of this paper is to provide a comprehensive overview of incidence, mortality, and survival rate for CRC.
Collapse
Affiliation(s)
- Rosa Marcellinaro
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| | - Domenico Spoletini
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| | - Michele Grieco
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| | - Pasquale Avella
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80138 Naples, Italy; (P.A.); (M.C.)
- Hepatobiliary and Pancreatic Surgery Unit, Pineta Grande Hospital, Castel Volturno, 81030 Caserta, Italy
| | - Micaela Cappuccio
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80138 Naples, Italy; (P.A.); (M.C.)
| | - Raffaele Troiano
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| | - Giorgio Lisi
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| | - Giovanni M. Garbarino
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| | - Massimo Carlini
- Department of General Surgery, S. Eugenio Hospital, 00144 Rome, Italy; (D.S.); (M.G.); (R.T.); (G.L.); (M.C.)
| |
Collapse
|
8
|
Crombé A, Spinnato P, Italiano A, Brisse HJ, Feydy A, Fadli D, Kind M. Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives. Diagn Interv Imaging 2023; 104:567-583. [PMID: 37802753 DOI: 10.1016/j.diii.2023.09.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.
Collapse
Affiliation(s)
- Amandine Crombé
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France; 'Sarcotarget' team, BRIC INSERM U1312 and Bordeaux University, 33000 Bordeaux France.
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | | | | | - Antoine Feydy
- Department of Radiology, Hopital Cochin-AP-HP, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - David Fadli
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France
| |
Collapse
|
9
|
Pacella G, Brunese MC, D’Imperio E, Rotondo M, Scacchi A, Carbone M, Guerra G. Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis. J Clin Med 2023; 12:7380. [PMID: 38068432 PMCID: PMC10707069 DOI: 10.3390/jcm12237380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 09/10/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. METHODS The PubMed database was searched for papers published in the English language no earlier than January 2018. RESULTS We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. CONCLUSIONS It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
Collapse
Affiliation(s)
- Giulia Pacella
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | - Maria Chiara Brunese
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | | | - Marco Rotondo
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| | - Andrea Scacchi
- General Surgery Unit, University of Milano-Bicocca, 20126 Milan, Italy
| | - Mattia Carbone
- San Giovanni di Dio e Ruggi d’Aragona Hospital, 84131 Salerno, Italy;
| | - Germano Guerra
- Department of Medicine and Health Science “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (G.P.)
| |
Collapse
|
10
|
Avella P, Cappuccio M, Cappuccio T, Rotondo M, Fumarulo D, Guerra G, Sciaudone G, Santone A, Cammilleri F, Bianco P, Brunese MC. Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives. Life (Basel) 2023; 13:2027. [PMID: 37895409 PMCID: PMC10608483 DOI: 10.3390/life13102027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM. METHODS A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened. RESULTS We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT). CONCLUSIONS Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.
Collapse
Affiliation(s)
- Pasquale Avella
- HPB Surgery Unit, Pineta Grande Hospital, Castel Volturno, 81030 Caserta, Italy;
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
| | - Micaela Cappuccio
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
| | - Teresa Cappuccio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Marco Rotondo
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Daniela Fumarulo
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Germano Guerra
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Guido Sciaudone
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | - Antonella Santone
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| | | | - Paolo Bianco
- HPB Surgery Unit, Pineta Grande Hospital, Castel Volturno, 81030 Caserta, Italy;
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy; (T.C.); (M.R.); (D.F.); (G.G.); (G.S.); (A.S.); (M.C.B.)
| |
Collapse
|
11
|
Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes. J Thorac Cardiovasc Surg 2023; 165:502-516.e9. [PMID: 36038386 DOI: 10.1016/j.jtcvs.2022.05.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/01/2022] [Accepted: 05/09/2022] [Indexed: 01/18/2023]
Abstract
OBJECTIVE For patients with thymic epithelial tumors, accurately predicting clinicopathological outcomes remains challenging. We aimed to investigate the performance of machine learning-based radiomic computed tomography phenotyping for predicting pathological (World Health Organization [WHO] type and TNM stage) and survival outcomes (overall and progression-free survival) in patients with thymic epithelial tumors. METHODS This retrospective study included patients with thymic epithelial tumors between January 2001 and January 2022. The radiomic features were extracted from preoperative unenhanced computed tomography images. After strict feature selection, random forest and random survival forest models were fitted to predict pathological and survival outcomes, respectively. The model performance was assessed by the area under the curve (AUC) and validated internally by the bootstrap method. RESULTS In total, 124 patients with a median age of 61 years were included. The radiomics random forest models of WHO type and TNM stage showed satisfactory performance with an AUCWHO of 0.898 (95% CI, 0.753-1.000) and an AUCTNM of 0.766 (95% CI, 0.642-0.886). For overall survival and progression-free survival prediction, the radiomics random survival forest models showed good performance (integrated AUCs, 0.923; 95% CI, 0.691-1.000 and 0.702; 95% CI, 0.513-0.875, respectively), and the integrated AUCs increased to 0.935 (95% CI, 0.705-1.000) and 0.811 (95% CI, 0.647-0.942), respectively, when combined with clinicopathological features. CONCLUSIONS Machine learning-based radiomic computed tomography phenotyping might allow for the satisfactory prediction of pathological and survival outcomes and further improve prognostic performance when integrated with clinicopathological features in patients with thymic epithelial tumors.
Collapse
|
12
|
Crombé A, Roulleau‐Dugage M, Italiano A. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1288-1313. [PMID: 36260064 PMCID: PMC9759765 DOI: 10.1002/cac2.12373] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/20/2022] [Accepted: 10/08/2022] [Indexed: 01/25/2023]
Abstract
Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring. Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings. Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides including a strong translational program that will allow for a better understanding of the determinants involved in sensitivity and treatment resistance to immune-oncology agents.
Collapse
Affiliation(s)
- Amandine Crombé
- Department of ImagingInstitut BergoniéBordeauxNouvelle‐AquitaineF‐33076France,Faculty of MedicineUniversity of BordeauxBordeauxNouvelle‐AquitaineF‐33000France
| | | | - Antoine Italiano
- Faculty of MedicineUniversity of BordeauxBordeauxNouvelle‐AquitaineF‐33000France,Early Phase Trials and Sarcoma UnitInstitut BergoniéBordeauxNouvelle‐AquitaineF‐33076France
| |
Collapse
|
13
|
De Sanctis R, Zelic R, Santoro A. Nomograms predicting local and distant recurrence and disease-specific mortality for R0/R1 soft tissue sarcomas of the extremities. Front Oncol 2022; 12:941896. [PMID: 36203418 PMCID: PMC9530899 DOI: 10.3389/fonc.2022.941896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Background Prognostic models for patients with soft tissue sarcoma (STS) of the extremities have been developed from large multi-institutional datasets with mixed results. We aimed to develop predictive nomograms for sarcoma-specific survival (SSS) and, for the first time, long-term local recurrence (LR) and distant recurrence (DR) in patients with STS of the extremities treated at our institution. Patients and methods Data from patients treated at Humanitas Cancer Center from 1997 to 2015 were analyzed. Variable selection was based on the clinical knowledge and multivariable regression splines algorithm. Perioperative treatments were always included in the model. Prognostic models were developed using Cox proportional hazards model, and model estimates were plotted in nomograms predicting SSS at 5 and 10 years and LR and DR at 2, 5, and 10 years. Model performance was estimated internally via bootstrapping, in terms of optimism-corrected discrimination (Harrell C-index) and calibration (calibration plots). Results Data on 517 patients were analyzed. At 5 and 10 years, SSS was 68.1% [95% confidence interval (CI), 63.8-72.1] and 55.6% (50.5-60.3), respectively. LR was 79.1% (95% CI, 75.3-82.4), 71.1% (95% CI, 66.7-75.1), and 66.0% (95% CI, 60.7-70.7) at 2, 5, and 10 years, respectively, whereas DR was 65.9% (95% CI, 61.6-69.9), 57.5% (95% CI, 53.0-61.8), and 52.1% (95% CI, 47.1-56.8) at 2, 5, and 10 years, respectively. SSS nomogram included age, gender, margins, tumor size, grading, and histotype. LR and DR nomograms incorporated mostly the same variables, except for age for DR; LR nomogram did not include gender but included anatomic site. The optimism-corrected C-indexes were 0.73 and 0.72 for SSS at 5 and 10 years, respectively; 0.65, 0.64, and 0.64 for LR at 2, 5, and 10 years, respectively; and 0.68 for DR at 2, 5, and 10 years. Predicted probabilities were close to the observed ones for all outcomes. Conclusions We developed and validated three nomograms for STS of the extremities predicting the probability of SSS at 5 and 10 years and LR and DR at 2, 5, and 10 years. By accounting for the perioperative treatment, these models allow prediction for future patients who had no perioperative treatment, thus being useful in the clinical decision-making process.
Collapse
Affiliation(s)
- Rita De Sanctis
- Medical Oncology and Hematology Unit, IRCCS Humanitas Research Hospital, Humanitas Cancer Center, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Renata Zelic
- Clinical Epidemiology Division, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Armando Santoro
- Medical Oncology and Hematology Unit, IRCCS Humanitas Research Hospital, Humanitas Cancer Center, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| |
Collapse
|