1
|
Guha S, Ibrahim A, Wu Q, Geng P, Chou Y, Yang H, Ma J, Lu L, Wang D, Schwartz LH, Xie CM, Zhao B. Machine learning-based identification of contrast-enhancement phase of computed tomography scans. PLoS One 2024; 19:e0294581. [PMID: 38306329 PMCID: PMC10836663 DOI: 10.1371/journal.pone.0294581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 11/04/2023] [Indexed: 02/04/2024] Open
Abstract
Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.
Collapse
Affiliation(s)
- Siddharth Guha
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Qian Wu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Pengfei Geng
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Yen Chou
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Jingchen Ma
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Delin Wang
- Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | | | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| |
Collapse
|
2
|
Gabiache G, Zadro C, Rozenblum L, Vezzosi D, Mouly C, Thoulouzan M, Guimbaud R, Otal P, Dierickx L, Rousseau H, Trepanier C, Dercle L, Mokrane FZ. Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas. Cancers (Basel) 2023; 15:4666. [PMID: 37760633 PMCID: PMC10526298 DOI: 10.3390/cancers15184666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data.
Collapse
Affiliation(s)
- Gildas Gabiache
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Charline Zadro
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Laura Rozenblum
- Department of Nuclear Medicine, Sorbonne Université, AP-HP, Hôpital La Pitié-Salpêtrière, 75013 Paris, France
| | - Delphine Vezzosi
- Department of Endocrinology, Rangueil University Hospital, 31400 Toulouse, France
| | - Céline Mouly
- Department of Endocrinology, Rangueil University Hospital, 31400 Toulouse, France
| | | | - Rosine Guimbaud
- Department of Oncology, Rangueil University Hospital, 31400 Toulouse, France
| | - Philippe Otal
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Lawrence Dierickx
- Department of Nuclear Medicine, IUCT-Oncopole, 31059 Toulouse, France;
| | - Hervé Rousseau
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| | - Christopher Trepanier
- New York-Presbyterian Hospital/Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Laurent Dercle
- New York-Presbyterian Hospital/Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Fatima-Zohra Mokrane
- Department of Radiology, Rangueil University Hospital, 31400 Toulouse, France (F.-Z.M.)
| |
Collapse
|
3
|
Rocha BA, Ferreira LC, Vianna LGR, Ferreira LGG, Ciconelle ACM, Da Silva Noronha A, Cortez Filho JM, Nogueira LSL, Leite JMRS, da Silva Filho MRM, da Costa Leite C, de Maria Felix M, Gutierrez MA, Nomura CH, Cerri GG, Carrilho FJ, Ono SK. Contrast phase recognition in liver computer tomography using deep learning. Sci Rep 2022; 12:20315. [PMID: 36434070 PMCID: PMC9700820 DOI: 10.1038/s41598-022-24485-y] [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: 06/13/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022] Open
Abstract
Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of São Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively.
Collapse
Affiliation(s)
- Bruno Aragão Rocha
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil ,Machiron Ltd., Rua Capote Valente, 671, São Paulo, 05409-002 Brazil
| | - Lorena Carneiro Ferreira
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil
| | | | | | | | | | - João Martins Cortez Filho
- grid.11899.380000 0004 1937 0722Department of Gastroenterology, University of São Paulo, School of Medicine (FMUSP), Hospital das Clínicas (HCFMUSP), Rua Dr. Enéas Carvalho de Aguiar, 225, São Paulo, SP 05403-000 Brazil
| | - Lucas Salume Lima Nogueira
- grid.11899.380000 0004 1937 0722Department of Gastroenterology, University of São Paulo, School of Medicine (FMUSP), Hospital das Clínicas (HCFMUSP), Rua Dr. Enéas Carvalho de Aguiar, 225, São Paulo, SP 05403-000 Brazil
| | | | - Maurício Ricardo Moreira da Silva Filho
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil
| | - Claudia da Costa Leite
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil
| | - Marcelo de Maria Felix
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil
| | - Marco Antônio Gutierrez
- grid.11899.380000 0004 1937 0722Informatics Department, The Heart Institute, Hospital das Clínicas (HCFMUSP), University of São Paulo, School of Medicine, Rua Dr. Enéas de Carvalho Aguiar 44, São Paulo, SP 05403-000 Brazil
| | - Cesar Higa Nomura
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil
| | - Giovanni Guido Cerri
- grid.11899.380000 0004 1937 0722InRad, Institute of Radiology, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campos, 75 Cerqueira César, São Paulo SP, 05403-010 Brazil
| | - Flair José Carrilho
- grid.11899.380000 0004 1937 0722Department of Gastroenterology, University of São Paulo, School of Medicine (FMUSP), Hospital das Clínicas (HCFMUSP), Rua Dr. Enéas Carvalho de Aguiar, 225, São Paulo, SP 05403-000 Brazil
| | - Suzane Kioko Ono
- grid.11899.380000 0004 1937 0722Department of Gastroenterology, University of São Paulo, School of Medicine (FMUSP), Hospital das Clínicas (HCFMUSP), Rua Dr. Enéas Carvalho de Aguiar, 225, São Paulo, SP 05403-000 Brazil
| |
Collapse
|
4
|
Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7842566. [PMID: 35434134 PMCID: PMC9010213 DOI: 10.1155/2022/7842566] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 01/29/2022] [Accepted: 03/06/2022] [Indexed: 02/07/2023]
Abstract
Purpose Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine.
Collapse
|
5
|
Sun SH, Eche T, Dorczynski C, Otal P, Revel-Mouroz P, Zadro C, Partouche E, Fares N, Maulat C, Bureau C, Schwartz LH, Rousseau H, Dercle L, Mokrane FZ. Predicting death or recurrence of portal hypertension symptoms after TIPS procedures. Eur Radiol 2022; 32:3346-3357. [PMID: 35015124 DOI: 10.1007/s00330-021-08437-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/25/2021] [Accepted: 10/24/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Accurate prediction of portal hypertension recurrence after transjugular intrahepatic portosystemic shunt (TIPS) placement will improve clinical decision-making. PURPOSE To evaluate if perioperative variables could predict disease-free survival (DFS) in cirrhotic patients with portal hypertension (PHT) treated with TIPS. MATERIALS AND METHODS We recruited 206 cirrhotic patients with PHT treated with TIPS, randomly assigned to training (n = 138) and validation (n = 68) sets. We recorded 7 epidemiological, 4 clinical, and 9 radiological variables. TIPS-distal end positioning (TIPS-DEP) measured the distance between the distal end of the stent and the hepatocaval junction on contrast-enhanced CT scans. In the training set, the signature was defined as the random forest for survival algorithm achieving the lowest error rate for the prediction of DFS which was landmarked 4 weeks after the TIPS procedure. In the training set, a simple to use scoring system was derived from variables selected by the signature. The primary endpoint was to assess if TIPS-DEP was associated with DFS. The secondary endpoint was to validate the scoring system in the validation set. RESULTS Overall, patients with TIPS-DEP ≥ 6 mm (n = 49) had a median DFS of 24.5 months vs. 72.8 months otherwise (n = 157, p = 0.004). In the training set, the scoring system was calculated by adding age ≥ 60 years old, Child-Pugh B or C, and TIPS-DEP ≥ 6 mm (1 point each) since the signature showed high DFS probability at 6.5 months post-landmark in patients that did not meet these criteria: 86%, 80%, and 78%, respectively. The hazard ratio [95 CI] between patients determined to be low-risk (< 2 points) and high-risk (≥ 2 points) was 2.30 [1.35-3.93] (p = 0.002) in the training set and 2.01 [0.94-4.32] (p = 0.072) in the validation set. CONCLUSION TIPS-DEP is an actionable radiological biomarker which can be combined with age and Child-Pugh score to predict death or PHT symptom recurrence after TIPS procedure. KEY POINTS • TIPS-DEP measurement was the third most important but only actionable variable for predicting DFS. • TIPS-DEP < 6 mm was associated with a DFS probability of 78% at 6.5 months post-landmark. • A simple scoring system calculated using age, Child-Pugh score, and TIPS-DEP predicted DFS after TIPS.
Collapse
Affiliation(s)
- Shawn H Sun
- Department of Radiology, Columbia University Vagellos College of Physicians and Surgeons, New York, NY, 10032, USA.,Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Thomas Eche
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Chloé Dorczynski
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Philippe Otal
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Paul Revel-Mouroz
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Charline Zadro
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Ephraim Partouche
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Nadim Fares
- Hepato-Gastroenterology Department, Purpan University Hospital, Toulouse, France
| | - Charlotte Maulat
- The Digestive Surgery and Liver Transplantation Department, Toulouse University Hospital, Toulouse, France
| | - Christophe Bureau
- Hepato-Gastroenterology Department, Purpan University Hospital, Toulouse, France
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Vagellos College of Physicians and Surgeons, New York, NY, 10032, USA.,Department of Radiology, New York Presbyterian Hospital, New York, NY, USA
| | - Hervé Rousseau
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France
| | - Laurent Dercle
- Department of Radiology, Columbia University Vagellos College of Physicians and Surgeons, New York, NY, 10032, USA. .,Department of Radiology, New York Presbyterian Hospital, New York, NY, USA.
| | - Fatima-Zohra Mokrane
- Radiology Department, Rangueil University Hospital, 1, avenue du Professeur Jean Poulhes, 31059, Toulouse, France.
| |
Collapse
|
6
|
Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021; 11:633176. [PMID: 33854969 PMCID: PMC8039446 DOI: 10.3389/fonc.2021.633176] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.
Collapse
Affiliation(s)
- Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States
| |
Collapse
|
7
|
Lu L, Sun SH, Afran A, Yang H, Lu ZF, So J, Schwartz LH, Zhao B. Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions. Tomography 2021; 7:55-64. [PMID: 33681463 PMCID: PMC7934702 DOI: 10.3390/tomography7010005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/17/2020] [Indexed: 02/06/2023] Open
Abstract
We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients. Pairs of CT images (baseline, 3-week post therapy) of 46 NSCLC patients with known EGFR mutation status were collected and a FDA-customized anthropomorphic thoracic phantom was scanned on two vendors' scanners at four different tube currents. Delta radiomics features were extracted from the NSCLC patient CTs and reproducible, non-redundant, and informative features were identified. The feature value differences between EGFR mutant and EGFR wildtype patients were quantitatively measured as the biological signal. Similarly, radiomics features were extracted from the phantom CTs. A pairwise comparison between settings resulted in a feature value difference that was quantitatively measured as the noise signal. Biological signals were compared to noise signals at each setting to determine if the distributions were significantly different by two-sample t-test, and thus robust. Four optimal features were selected to predict EGFR mutation status, Tumor-Mass, Sigmoid-Offset-Mean, Gabor-Energy and DWT-Energy, which quantified tumor mass, tumor-parenchyma density transition at boundary, line-like pattern inside tumor and intratumoral heterogeneity, respectively. The first three variables showed robustness across the majority of studied CT acquisition parameters. The textual feature DWT-Energy was less robust. The proposed framework was able to determine robustness of radiomics features at specific settings by comparing biological signal to noise signal. Identification of robust radiomics features may improve the generalizability of radiomics models in future studies.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY 10032, USA; (L.L.); (S.H.S.); (A.A.); (H.Y.); (Z.F.L.); (J.S.); (L.H.S.)
| |
Collapse
|
8
|
Nakanishi R, Oki E, Hasuda H, Sano E, Miyashita Y, Sakai A, Koga N, Kuriyama N, Nonaka K, Fujimoto Y, Jogo T, Hokonohara K, Hu Q, Hisamatsu Y, Ando K, Kimura Y, Yoshizumi T, Mori M. Radiomics Texture Analysis for the Identification of Colorectal Liver Metastases Sensitive to First-Line Oxaliplatin-Based Chemotherapy. Ann Surg Oncol 2021; 28:2975-2985. [PMID: 33454878 DOI: 10.1245/s10434-020-09581-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/01/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVE The aim of this study was to develop a radiomics-based prediction model for the response of colorectal liver metastases to oxaliplatin-based chemotherapy. METHODS Forty-two consecutive patients treated with oxaliplatin-based first-line chemotherapy for colorectal liver metastasis at our institution from August 2013 to October 2019 were enrolled in this retrospective study. Overall, 126 liver metastases were chronologically divided into the training (n = 94) and validation (n = 32) cohorts. Regions of interest were manually segmented, and the best response to chemotherapy was decided based on Response Evaluation Criteria in Solid Tumors (RECIST). Patients who achieved clinical complete and partial response according to RECIST were defined as good responders. Radiomics features were extracted from the pretreatment enhanced computed tomography scans, and a radiomics score was calculated using the least absolute shrinkage and selection operator regression model in a trial cohort. RESULTS The radiomics score significantly discriminated good responders in both the trial (area under the curve [AUC] 0.8512, 95% confidence interval [CI] 0.7719-0.9305; p < 0.0001) and validation (AUC 0.7792, 95% CI 0.6176-0.9407; p < 0.0001) cohorts. Multivariate analysis revealed that high radiomics scores greater than - 0.06 (odds ratio [OR] 23.803, 95% CI 8.432-80.432; p < 0.0001), clinical non-T4 (OR 6.054, 95% CI 2.164-18.394; p = 0.0005), and metachronous disease (OR 11.787, 95% CI 2.333-70.833; p = 0.0025) were independently associated with good response. CONCLUSIONS Radiomics signatures may be a potential biomarker for the early prediction of chemosensitivity in colorectal liver metastases. This approach may support the treatment strategy for colorectal liver metastasis.
Collapse
Affiliation(s)
- Ryota Nakanishi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Eiji Oki
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hirofumi Hasuda
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Eiki Sano
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yu Miyashita
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akihiro Sakai
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naomichi Koga
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naotaka Kuriyama
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kentaro Nonaka
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshiaki Fujimoto
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomoko Jogo
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kentaro Hokonohara
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Qingjiang Hu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yuichi Hisamatsu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koji Ando
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yasue Kimura
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomoharu Yoshizumi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masaki Mori
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| |
Collapse
|
9
|
Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2020; 188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.
Collapse
Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, USA
| | - Theophraste Henry
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Alexandre Carré
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | | | - Eric Deutsch
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Charlotte Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France.
| |
Collapse
|