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Chen H, Chen Y, Dong Y, Gou L, Hu Y, Wang Q, Li G, Li S, Yu J. 18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer. Ann Surg Oncol 2024:10.1245/s10434-024-15631-z. [PMID: 38976160 DOI: 10.1245/s10434-024-15631-z] [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: 10/16/2023] [Accepted: 06/05/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). METHODS A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. RESULTS The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). CONCLUSIONS The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.
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Affiliation(s)
- Hao Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yi Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Ye Dong
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Longfei Gou
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yanfeng Hu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Guoxin Li
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Shulong Li
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China.
| | - Jiang Yu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
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Li B, Su J, Liu K, Hu C. Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer. Eur J Radiol Open 2024; 12:100549. [PMID: 38304572 PMCID: PMC10831499 DOI: 10.1016/j.ejro.2024.100549] [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: 09/10/2023] [Revised: 01/03/2024] [Accepted: 01/14/2024] [Indexed: 02/03/2024] Open
Abstract
Purpose Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC. Methods A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top significant features. Then we developed a radiomics model, a deep learning model, and a fusion model based on the selected features. The performance of three models were compared by the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results Of the patients, 42 patients were PD-L1 negative and 94 patients were PD-L1 positive. A total of 2446 radiomics features and 4096 deep learning features were extracted per patient. In the training dataset, the fusion model achieved a highest AUC (0.954, 95% confident internal [CI]: 0.890-0.986) compared with the radiomics model (0.829, 95%CI: 0.738-0.898) and the deep learning model (0.935, 95%CI: 0.865-0.975). In the validation dataset, the AUC of the fusion model (0.910, 95% CI: 0.779-0.977) was also higher than that of the radiomics model (0.785, 95% CI: 0.628-0.897) and the deep learning model (0.867, 95% CI: 0.724-0.952). Conclusion The PET/CT-based deep learning radiomics model can predict the PD-L1 expression accurately in NSCLC patients, and provides a non-invasive tool for clinicians to select positive PD-L1 patients.
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Affiliation(s)
| | | | - Kai Liu
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China
| | - Chunfeng Hu
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China
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Fu C, Zhang B, Guo T, Li J. Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques. Korean J Radiol 2024; 25:86-102. [PMID: 38184772 PMCID: PMC10788608 DOI: 10.3348/kjr.2023.0840] [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: 06/22/2023] [Revised: 09/28/2023] [Accepted: 10/08/2023] [Indexed: 01/08/2024] Open
Abstract
Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.
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Affiliation(s)
- Chen Fu
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
| | - Bangxing Zhang
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Tiankang Guo
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Junliang Li
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China.
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [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: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
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Xie J, Xue B, Bian S, Ji X, Lin J, Zheng X, Tang K. A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer. Nucl Med Commun 2023; 44:977-987. [PMID: 37578301 DOI: 10.1097/mnm.0000000000001742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
PURPOSE Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC. METHOD In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram. RESULT Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively. CONCLUSION The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.
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Affiliation(s)
- Jiageng Xie
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Beihui Xue
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shuying Bian
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaowei Ji
- Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jie Lin
- Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiangwu Zheng
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Kun Tang
- Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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Ho SYA, Tay KV. Systematic review of diagnostic tools for peritoneal metastasis in gastric cancer-staging laparoscopy and its alternatives. World J Gastrointest Surg 2023; 15:2280-2293. [PMID: 37969710 PMCID: PMC10642463 DOI: 10.4240/wjgs.v15.i10.2280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/16/2023] [Accepted: 06/12/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Gastric cancer is one of the leading causes of cancer burden and mortality, often resulting in peritoneal metastasis in advanced stages with negative survival outcomes. Staging laparoscopy has become standard practice for suspected cases before a definitive gastrectomy or palliation. This systematic review aims to compare the efficacy of other diagnostic modalities instead of staging laparoscopy as the alternatives are able to reduce cost and invasive staging procedures. Recently, a radiomic model based on computed tomography and positron emission tomography (PET) has also emerged as another method to predict peritoneal metastasis. AIM To determine if the efficacy of computed tomography, magnetic resonance imaging and PET is comparable with staging laparoscopy. METHODS Articles comparing computed tomography, PET, magnetic resonance imaging, and radiomic models based on computed tomography and PET to staging laparoscopies were filtered out from the Cochrane Library, EMBASE, PubMed, Web of Science, and Reference Citations Analysis (https://www.referencecitationanalysis.com/). In the search for studies comparing computed tomography (CT) to staging laparoscopy, five retrospective studies and three prospective studies were found. Similarly, five retrospective studies and two prospective studies were also included for papers comparing CT to PET scans. Only one retrospective study and one prospective study were found to be suitable for papers comparing CT to magnetic resonance imaging scans. RESULTS Staging laparoscopy outperformed computed tomography in all measured aspects, namely sensitivity, specificity, positive predictive value and negative predictive value. Magnetic resonance imaging and PET produced mixed results, with the former shown to be only marginally better than computed tomography. CT performed slightly better than PET in most measured domains, except in specificity and true negative rates. We speculate that this may be due to the limited F-fluorodeoxyglucose uptake in small peritoneal metastases and in linitis plastica. Radiomic modelling, in its current state, shows promise as an alternative for predicting peritoneal metastases. With further research, deep learning and radiomic modelling can be refined and potentially applied as a preoperative diagnostic tool to reduce the need for invasive staging laparoscopy. CONCLUSION Staging laparoscopy was superior in all measured aspects. However, associated risks and costs must be considered. Refinements in radiomic modelling are necessary to establish it as a reliable screening technique.
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Affiliation(s)
| | - Kon Voi Tay
- Upper GI and Bariatric Division, General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Upper GI and Bariatric Division, General Surgery, Woodlands Health, Singapore 768024, Singapore
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Miccichè F, Rizzo G, Casà C, Leone M, Quero G, Boldrini L, Bulajic M, Corsi DC, Tondolo V. Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review. Front Med (Lausanne) 2023; 10:1189740. [PMID: 37663653 PMCID: PMC10469447 DOI: 10.3389/fmed.2023.1189740] [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: 03/19/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Gastric cancer (GC) is an aggressive and clinically heterogeneous tumor, and better risk stratification of lymph node metastasis (LNM) could lead to personalized treatments. The role of radiomics in the prediction of nodal involvement in GC has not yet been systematically assessed. This study aims to assess the role of radiomics in the prediction of LNM in GC. Methods A PubMed/MEDLINE systematic review was conducted to assess the role of radiomics in LNM. The inclusion criteria were as follows: i. original articles, ii. articles on radiomics, and iii. articles on LNM prediction in GC. All articles were selected and analyzed by a multidisciplinary board of two radiation oncologists and one surgeon, under the supervision of one radiation oncologist, one surgeon, and one medical oncologist. Results A total of 171 studies were obtained using the search strategy mentioned on PubMed. After the complete selection process, a total of 20 papers were considered eligible for the analysis of the results. Radiomics methods were applied in GC to assess the LNM risk. The number of patients, imaging modalities, type of predictive models, number of radiomics features, TRIPOD classification, and performances of the models were reported. Conclusions Radiomics seems to be a promising approach for evaluating the risk of LNM in GC. Further and larger studies are required to evaluate the clinical impact of the inclusion of radiomics in a comprehensive decision support system (DSS) for GC.
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Affiliation(s)
- Francesco Miccichè
- U.O.C. di Radioterapia Oncologica, Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | - Gianluca Rizzo
- U.O.C. di Chirurgia Digestiva e del Colon-Retto, Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | - Calogero Casà
- U.O.C. di Radioterapia Oncologica, Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | - Mariavittoria Leone
- U.O.C. di Radioterapia Oncologica, Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | - Giuseppe Quero
- U.O.C. di Chirurgia Digestiva, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- U.O.C. di Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Milutin Bulajic
- U.O.C. di Endoscopia Digestiva, Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | | | - Vincenzo Tondolo
- U.O.C. di Chirurgia Digestiva e del Colon-Retto, Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
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Pullen LCE, Noortman WA, Triemstra L, de Jongh C, Rademaker FJ, Spijkerman R, Kalisvaart GM, Gertsen EC, de Geus-Oei LF, Tolboom N, de Steur WO, Dantuma M, Slart RHJA, van Hillegersberg R, Siersema PD, Ruurda JP, van Velden FHP, Vegt E. Prognostic Value of [ 18F]FDG PET Radiomics to Detect Peritoneal and Distant Metastases in Locally Advanced Gastric Cancer-A Side Study of the Prospective Multicentre PLASTIC Study. Cancers (Basel) 2023; 15:cancers15112874. [PMID: 37296837 DOI: 10.3390/cancers15112874] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/13/2023] [Accepted: 05/14/2023] [Indexed: 06/12/2023] Open
Abstract
AIM To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [18F]FDG-PET radiomics. METHODS [18F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed. RESULTS None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance. CONCLUSION Overall, [18F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.
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Affiliation(s)
- Lieke C E Pullen
- Biomedical Photonic Imaging Group, University of Twente, 7522 NB Enschede, The Netherlands
| | - Wyanne A Noortman
- Biomedical Photonic Imaging Group, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Radiology, Leiden University Medical Center, 2333 ZD Leiden, The Netherlands
| | - Lianne Triemstra
- Department of Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Cas de Jongh
- Department of Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Fenna J Rademaker
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Romy Spijkerman
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Gijsbert M Kalisvaart
- Department of Radiology, Leiden University Medical Center, 2333 ZD Leiden, The Netherlands
| | - Emma C Gertsen
- Department of Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Biomedical Photonic Imaging Group, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Radiology, Leiden University Medical Center, 2333 ZD Leiden, The Netherlands
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Wobbe O de Steur
- Department of Surgery, Leiden University Medical Center, 2333 ZD Leiden, The Netherlands
| | - Maura Dantuma
- Multi-Modality Medical Imaging Group, TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Riemer H J A Slart
- Biomedical Photonic Imaging Group, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | | | - Peter D Siersema
- Department of Gastroenterology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Jelle P Ruurda
- Department of Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Floris H P van Velden
- Department of Radiology, Leiden University Medical Center, 2333 ZD Leiden, The Netherlands
| | - Erik Vegt
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
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Construction of a nomogram model for predicting peritoneal metastasis in gastric cancer: focused on cardiophrenic angle lymph node features. Abdom Radiol (NY) 2023; 48:1227-1236. [PMID: 36807997 PMCID: PMC10115726 DOI: 10.1007/s00261-023-03848-7] [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: 11/07/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/20/2023]
Abstract
BACKGROUND A different treatment was used when peritoneal metastases (PM) occurred in patients with gastric cancer (GC). Certain cancers' peritoneal metastasis could be predicted by the cardiophrenic angle lymph node (CALN). This study aimed to establish a predictive model for PM of gastric cancer based on the CALN. METHODS Our center retrospectively analyzed all GC patients between January 2017 and October 2019. Pre-surgery computed tomography (CT) scans were performed on all patients. The clinicopathological and CALN features were recorded. PM risk factors were identified via univariate and multivariate logistic regression analyses. The receiver operator characteristic (ROC) curves were generated using these CALN values. Using the calibration plot, the model fit was assessed. A decision curve analysis (DCA) was conducted to assess the clinical utility. RESULTS 126 of 483 (26.1%) patients were confirmed as having peritoneal metastasis. These relevant factors were associated with PM: age, sex, T stage, N stage, enlarged retroperitoneal lymph nodes (ERLN), CALN, the long diameter of the largest CALN (LD of LCALN), the short diameter of the largest CALN (SD of LCALN), and the number of CALNs (N of CALNs). The multivariate analysis illustrated that the LD of LCALN (OR = 2.752, p < 0.001) was PM's independent risk factor in GC patients. The area under the curve (AUC) of the model was 0.907 (95% CI 0.872-0.941), demonstrating good performance in the predictive value of PM. There is excellent calibration evident from the calibration plot, which is close to the diagonal. The DCA was presented for the nomogram. CONCLUSION CALN could predict gastric cancer peritoneal metastasis. The model in this study provided a powerful predictive tool for determining PM in GC patients and helping clinicians allocate treatment.
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10
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Sui C, Lin C, Tao T, Guan W, Zhang H, Tao L, Wang M, Wang F. Prognostic significance of serum CA125 in the overall management for patients with gastrointestinal stromal tumors. BMC Gastroenterol 2023; 23:25. [PMID: 36703123 PMCID: PMC9878806 DOI: 10.1186/s12876-023-02655-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 01/17/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Carbohydrate antigen 125 (CA125) is elevated as a tumor marker in many carcinomas, but its association with gastrointestinal stromal tumor (GIST) has received less attention. This study intends to evaluate whether CA125 level can predict tumor progression and overall survival (OS) of GIST patients. METHODS We retrospectively analyzed the clinical data and follow-up records of GIST patients who underwent surgical resection in Nanjing Drum Tower Hospital from August 2010 to December 2020. All patients were classified according to serum CA125 level. The relationship between CA125 and clinical outcomes was then examined. RESULTS A total of 406 GIST patients were enrolled in this study, among which 46 patients had preoperative elevated serum CA125 level and 13 patients with high CA125 level both preoperative and postoperative were observed. Preoperative CA125 concentration was significantly related to rupture status, resection style, tumor site, tumor size, mitotic index, NIH risk grade and c-kit exons. According to Kaplan-Meier curve analysis, high expression of postoperative CA125 was significantly correlated with worse progression-free survival (PFS) and OS among patients with preoperative elevated CA125 level. Ultimately, Cox proportional regression model analysis revealed that increase of preoperative and concurrent postoperative CA125 concentration was an independent predictive factor for PFS. CONCLUSIONS The concurrent abnormality of serum CA125 before and after operation was an independent risk factor for GIST progression, suggesting its significance as a serum biomarker in the overall management of GIST patients.
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Affiliation(s)
- Chao Sui
- grid.41156.370000 0001 2314 964XMedical School of Nanjing University, Nanjing, China ,grid.412676.00000 0004 1799 0784Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Chen Lin
- grid.410745.30000 0004 1765 1045Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Tingting Tao
- grid.412676.00000 0004 1799 0784Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Wenxian Guan
- grid.41156.370000 0001 2314 964XMedical School of Nanjing University, Nanjing, China ,grid.412676.00000 0004 1799 0784Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Haoran Zhang
- grid.412676.00000 0004 1799 0784Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Liang Tao
- grid.41156.370000 0001 2314 964XMedical School of Nanjing University, Nanjing, China ,grid.410745.30000 0004 1765 1045Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Meng Wang
- grid.41156.370000 0001 2314 964XMedical School of Nanjing University, Nanjing, China ,grid.410745.30000 0004 1765 1045Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Feng Wang
- grid.41156.370000 0001 2314 964XMedical School of Nanjing University, Nanjing, China ,grid.410745.30000 0004 1765 1045Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
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11
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Altini C, Maggialetti N, Branca A, Pisani AR, Rubini D, Sardaro A, Stabile Ianora AA, Rubini G. 18F-FDG PET/CT in peritoneal tumors: a pictorial review. Clin Transl Imaging 2023. [DOI: 10.1007/s40336-022-00534-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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12
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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13
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Dai W, Li Y, Huang Z, Lin C, Zhang XX, Xia W. Predictive factors and nomogram to evaluate the risk of below-ankle re-amputation in patients with diabetic foot. Curr Med Res Opin 2022; 38:1823-1829. [PMID: 36107826 DOI: 10.1080/03007995.2022.2125257] [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] [Indexed: 11/03/2022]
Abstract
BACKGROUND Diabetes mellitus, as the most common metabolic disease, is common worldwide and represents a crucial global health concern. The purpose of this research was to investigate the related risk factors and to develop a re-amputation risk nomogram in diabetic patients who have undergone an amputation. METHODS A observational analysis was performed on 459 patients who have underwent amputation for diabetic foot from January 2014 through December 2019 at the First Affiliated Hospital of Wenzhou Medical University. The least absolute shrinkage and selection operator regression and stepwise regression methods were implemented to determine risk selection for the re-amputation risk model, and the predictive nomogram was established with these features. Calibration curve, receiver operating characteristic curve, and decision curve analysis of this re-amputation nomogram were assessed. RESULTS Predictors contained in this predictive model included smoking, glycated hemoglobin A1c (HbA1c), ankle-brachial index (ABI) and C-reactive protein (CRP). Good discrimination with a C-index of 0.725 (95% CI, 0.6624-0.7876) and good calibration were displayed with this predictive model. The decision curve analysis showed that this re-amputation nomogram predicting risk adds more benefit than none strategy if the threshold probability of a patient was >6% and <59%. CONCLUSIONS This novel re-amputation nomogram incorporating smoking, glycated hemoglobin A1c (HbA1c), ankle-brachial index (ABI), C-reactive protein (CRP), and smoking could be easily used to predict individual re-amputation risk prediction in diabetic foot patients who have undergone an amputation. In the future, further analysis and external testing will be needed as much as possible to reconfirm that this new Nomogram can accurately predict the risk of toe re-amputation.
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Affiliation(s)
- Wentong Dai
- Burn and Wound Healing Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuan Li
- Burn and Wound Healing Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zexin Huang
- Department of Endocrinology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Cai Lin
- Burn and Wound Healing Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xing-Xing Zhang
- Department of Endocrinology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Weidong Xia
- Burn and Wound Healing Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Li Y, Xie F, Xiong Q, Lei H, Feng P. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:946038. [PMID: 36059703 PMCID: PMC9433672 DOI: 10.3389/fonc.2022.946038] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/01/2022] [Indexed: 01/19/2023] Open
Abstract
Objective To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models. Methods PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool. Results A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction. Conclusion ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752
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15
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Ahn H, Song GJ, Jang SH, Lee HJ, Lee MS, Lee JH, Oh MH, Jeong GC, Lee SM, Lee JW. Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers. Cancers (Basel) 2022; 14:cancers14163936. [PMID: 36010928 PMCID: PMC9406203 DOI: 10.3390/cancers14163936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/07/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
The relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) textural features and histopathological findings in gastric cancer has not been fully evaluated. We investigated the relationship between the textural features of primary tumors on FDG PET/CT with histopathological findings and recurrence-free survival (RFS) in patients with advanced gastric cancer (AGC). Fifty-six patients with AGC who underwent FDG PET/CT for staging work-ups were retrospectively enrolled. Conventional parameters and the first- and second-order textural features of AGC were extracted using PET textural analysis. Upon histopathological analysis, along with histopathological classification and staging, the degree of CD4, CD8, and CD163 cell infiltrations and expressions of interleukin-6 and matrix-metalloproteinase-11 (MMP-11) in the primary tumor were assessed. The histopathological classification, Lauren classification, lymph node metastasis, CD8 T lymphocyte and CD163 macrophage infiltrations, and MMP-11 expression were significantly associated with the textural features of AGC. The multivariate survival analysis showed that increased FDG uptake and intra-tumoral metabolic heterogeneity were significantly associated with an increased risk of recurrence after curative surgery. Textural features of AGC on FDG PET/CT showed significant correlations with the inflammatory response in the tumor microenvironment and histopathological features of AGC, and they showed significant prognostic values for predicting RFS.
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Affiliation(s)
- Hyein Ahn
- Department of Pathology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Geum Jong Song
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Si-Hyong Jang
- Department of Pathology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Hyun Ju Lee
- Department of Pathology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Moon-Soo Lee
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Ji-Hye Lee
- Department of Pathology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Mee-Hye Oh
- Department of Pathology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Geum Cheol Jeong
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Korea
- Correspondence: (S.M.L.); (J.W.L.); Tel.: +82-41-570-3540 (S.M.L.); +82-32-290-2975 (J.W.L.)
| | - Jeong Won Lee
- Department of Nuclear Medicine, College of Medicine, Catholic Kwandong University, International St. Mary’s Hospital, 25 Simgok-ro 100-gil, Seo-gu, Incheon 22711, Korea
- Correspondence: (S.M.L.); (J.W.L.); Tel.: +82-41-570-3540 (S.M.L.); +82-32-290-2975 (J.W.L.)
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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