Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

Fan, Lijing; Li, Jing; Zhang, Huiling; Yin, Hongkun; Zhang, Rongguo; Zhang, Jibin; Chen, Xuejun

doi:10.1007/s00261-021-03315-1

Cited by 24 publications

(19 citation statements)

References 56 publications

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“…A total of 7 studies on gastric cancer were found [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ], all using 18F-FDG as radiopharmaceutical. The average number of patients included was 163.7 (range 79–214), with 5/7 (71.4%) studies including more than 100 patients, 5/7 (71.4%) using a separate validation dataset and 1/7 (14.3%) using prospective data.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, 4 studies were conducted for diagnostic purposes: 2 for nodal involvement prediction (AUC between 0.74 and 0.81) [ 29 , 35 ], 1 for peritoneal involvement prediction (AUC 0.88 in the validation cohorts) [ 30 ] and 1 to differentiate between gastric cancer and primary gastric lymphoma (AUC 0.77) [ 31 ]. The remaining three were prognosis-oriented [ 32 , 33 , 34 ].…”

Section: Resultsmentioning

confidence: 99%

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Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

et al. 2022

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

et al. 2022

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“…Similarly in Bang’s study, 35 among the 18 ML models, the XBoost classifier showed the best performance in early gastric cancer prediction and survivability with the accuracy of 93.4%, precision of 92.6%, recall of 99.0%, and F1 score of 95.7%. Fan et al 36 retrospectively compared 3 ML techniques for the prediction of metastatic, relapse, and patient survival chances in the early stage of gastric cancer. In their study, the AdaBoost model achieved better performance with the AUC of 0.849.…”

Section: Discussionmentioning

confidence: 99%

“…Presently, ML can predict breast cancer survivability in the primary stages. 34 - 36 Das et al 37 and Hauser et al 38 have compared selected ML methods to the survival prognosis of patients with leukemia. They have respectively found that the gradient boosting algorithms (BAs) such hist gradient boosting (HGB) with area under the curve (AUC) of 0.779 and XGBoost with AUC of 0.87 achieve the highest performance.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Afrash

Yazdani

Kazemi-Arpanahi³

2022

Clin Med Insights Oncol

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Background: Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. Methods: A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. Results: The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance. Conclusions: The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.

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“…Arai et al (2022) applied ML method to predict the risk of GC in patients by information on gastric atrophy and intestinal metaplasia at the initial EGD [11]. Fan et al used ML-based approaches to predict of Lymphovascular invasion status (LVI), which is related to metastasis and poor survival in GC patients [12]. Also, an advanced model was expanded to comprise radiomics clinical and features attributes to boost the performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Predicting metastasis in Gastric cancer patients: machine learning-based approaches

Talebi

Celis‐Morales

Borumandnia

et al. 2022

Preprint

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Background Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. The study aimed to perform machine learning (ML) methods in GC patients. Methods The data applied in this study including 733 of GC patients diagnosed at Taleghani hospital. In order to predict metastasis in GC, machine learning approaches, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Regression Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, precision, sensitivity, specificity and AUC of Receiver operating characteristic (ROC) curve were obtained. Results 262 (36%) experienced metastasis among 733 patients with GC. The RF of ML-based models, with size of tomur and age as two essential variables, is considered as efficient model, because of higher specificity and AUC (84% and 87%). Also, the sensitivity in SVM model seems to be better (93%). Conclusion According to AUC, sensitivity and specificity, both RF and SVM can be regarded as better ML-based algorithms among six applied ML-based methods.

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Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables

Cited by 24 publications

References 56 publications

Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Predicting metastasis in Gastric cancer patients: machine learning-based approaches

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