Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

Li, Jing; Dong, Di; Fang, Mengjie; Wang, Rui; Tian, Jie; Li, Hailiang; Gao, Jianbo

doi:10.1007/s00330-019-06621-x

Cited by 129 publications

(92 citation statements)

References 34 publications

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“…Radiomics utilizes automated quantitative characterization algorithms to transform a large number of excavatable spatial ROI-based image data into representative and effective radiomic features [ 6 ]. Recent advancements in radiomics have provided new ideas for individualized management of GC, including lymphatic metastasis prediction [ 7 , 8 ], distant metastasis prediction [ 9 ], therapeutic response evaluation [ 10 ], and prognostic evaluation [ 11 , 12 ]. These studies highlighted the value of radiomics, suggesting that radiomics could be a potential tool for the Lauren classification in GC.…”

Section: Introductionmentioning

confidence: 99%

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

et al. 2020

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Background Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. Methods A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. Results The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusions The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.

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Section: Introductionmentioning

confidence: 99%

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

et al. 2020

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“…(2020) [40] NR Skeletal metastasis did not meet the criteria of the term ‘disseminated’; no skeletal metastasis 54/54 54(NR)/0(NR) NR NR Jing Li et al. (2020) [41] Patients underwent gastrectomy plus lymph node dissection and were diagnosed gastric adenocarcinomas; patients were scanned with GSI mode; without any local or systematic treatment before CT scans and surgery; with definite postoperative pathologic data. Invisible lesion on CT images; with a minimum diameter of tumor less than 5 mm insufficient to outline a valid ROI; insufficient stomach distension; poor image quality for post-processing.…”

Section: Resultsmentioning

confidence: 99%

“…(2020) [40] DM Disseminated skeletal metastasis prostate cancer( n = 12), GC=( n = 12), breast cancers( n = 15), miscellaneous cancers ( n = 10) Expert consensus NR YES Jing Li et al. (2020) [41] LNM LNM in GC GC Histopathology; follow up Resampling method NO L. Zhang et al. (2020) [42] DM Lung metastasis in STS STS Histopathology Random split sample validation NO Li-Qiang Zhou et al.…”

Section: Resultsmentioning

confidence: 99%

“…(2020) [40] BS NR NR NO NR ANN NR Retrospective clinical data from cancer institute hospital, Tokyo, Japan NR/54 2013.1–2019.8 NO Jing Li et al. (2020) [41] dual-energy CT YES NR YES DCNNs; ANN; Ksvm CNN; ANN; SVM NR Retrospective cohort 136/68 2012.1–2018.11 NO L. Zhang et al. (2020) [42] MRI, CT NR NR NO Inception V3 CNN; Inception YES Data collected from Cancer Imaging Archive 25/15 NR YES Li-Qiang Zhou et al.…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

et al. 2021

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Background Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. Methods We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. Findings We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79–84%), specificity of 84% (82–87%) and AUC of 0·90 (0·87–0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83–90%) for machine learning and 86% (82–89%) for deep learning, and a pooled specificity of 89% (82–93%) for machine learning, and 87% (82–91%) for deep learning. Interpretation AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. Funding College students' innovative entrepreneurial training plan program .

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“…Delta radiomics, defined as the changes in features according to the dynamic process that occur during therapeutic courses makes possible personalized predictions for chemotherapeutic benefits [13]. Besides, dual-energy computed tomography (DECT) is a promising modality for stomach imaging due to its potential to provide a large amount of pathophysiological information [14] and no prior studies have investigated the role of DECT in GC using radiomics, except for one that applied DECT radiomics to predict lymph node metastasis [15].…”

Section: Ivyspringmentioning

confidence: 99%

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

Tan¹,

Wang²,

Chen³

et al. 2020

J. Cancer

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Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.

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Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

Cited by 129 publications

References 34 publications

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

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