Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis

Hou, Zeyu; Yang, Yang; Li, Shuangshuang; Yan, Jing; Ren, Wei; Liu, Juan; Wang, Kangxin; Liu, Baorui; Wan, Shui

doi:10.21037/qims.2018.05.01

Cited by 37 publications

(34 citation statements)

References 31 publications

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“…Therefore, it could be interesting to investigate in the future if different fluid-sensitive fatsaturated MRI techniques could provide and support similar results using the machine learning approach. We also encourage future studies exploring the potential of radiomics [33,34] in the evaluation of inflammatory sacroiliitis, with the potential impact of deriving new diagnostic and prognostic information.…”

Section: Discussionmentioning

confidence: 96%

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

et al. 2020

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Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. Methods: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with~80% (46 samples, 20 positive and 26 negative) as training and~20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. Results: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. Conclusions: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.

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Section: Discussionmentioning

confidence: 96%

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

et al. 2020

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“…17,18 The four MDCT-derived radiomic features were consistent with previous biomarker findings for gastric cancer or survival. [32][33][34] Given the radiomics hypothesis, greater major axis length and median intensity values demonstrate larger tumor size, probably in accord with higher disease occurrence probability and poorer prognosis. Lower small zone emphasis values and greater zone size nonuniformity values are possibly indicative of more intratumor heterogeneity and worse survival.…”

Section: Discussionmentioning

confidence: 98%

Preoperative computed tomography‐guided disease‐free survival prediction in gastric cancer: a multicenter radiomics study

et al. 2020

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Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis. Methods: A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use. Results: In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage. Conclusions: The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.

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“…According to the Act, LST implementation should precede assessment of the EOL process (12). However, prognostic uncertainty due to the underlying disease fundamentally exists, causing di culty for doctors to correctly assess the EOL process (33).…”

Section: Discussionmentioning

confidence: 99%

“…Although these situations lower the quality of life at the EOL, legal regulations or policies regarding LST discussions were nonexistent before the LST Decisions Act for patients in hospice and palliative care or at the EOL was enacted on February 4, 2018 (12). The Act aims to assure the best interest of the patients and to respect their self-determination rights.…”

Section: Introductionmentioning

confidence: 99%

Difficulties doctors experience during life-sustaining treatment discussion after enactment of the Life-Sustaining Treatment Decisions Act: A cross-sectional study

Yoo

Choi

Kim

et al. 2020

Preprint

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Background In the Republic of Korea, life-sustaining treatment (LST) for patients at the end of life should be legally discussed by a doctor according to the LST Decisions Act, which was enacted in February 2018. However, little is known about challenges doctors experience during LST discussion. This study aims to investigate the perceived difficulties of doctors during LST discussions after enactment of the new Act. Methods A cross-sectional survey was conducted in a tertiary hospital in August 2019 and included 686 doctors (432 attending physicians, 141 clinical fellows, and 295 residents) who care for seriously ill patients. The doctors were given a structured questionnaire, and difficulties during the discussion process were examined. Results A total of 132 doctors answered the questionnaire. Most (86.4%) experienced considerable difficulties during LST discussions (mean score, 7.4 ± 1.6/10). The two most common difficulties were communication with patients and family and determining when to discuss LST. Two-thirds of doctors found direct discussions with the patient difficult and said they would initiate LST discussions only with family. LST discussions were actually initiated later than considered appropriate. When medically assessing whether the patient is imminently dying, 56% of doctors experienced disagreements with other doctors, which could affect their decisions. Conclusion In the present study, most doctors experienced serious difficulties during LST discussions. To alleviate these difficulties, further institutional support is needed.

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Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis

Abstract: Pretreatment radiomic analysis using CECT can potentially provide important information regarding the therapeutic response to PLDRT for GCACM, improving risk stratification.

Cited by 37 publications

References 31 publications

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

Preoperative computed tomography‐guided disease‐free survival prediction in gastric cancer: a multicenter radiomics study

Difficulties doctors experience during life-sustaining treatment discussion after enactment of the Life-Sustaining Treatment Decisions Act: A cross-sectional study

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