A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study

Cui, Yanfen; Zhang, Jiayi; Li, Zhenhui; Wei, Kaikai; Ye, Lei; Ren, Jialiang; Wu, Lei; Shi, Zhenwei; Meng, Xiaochun; Yang, Xiaotang; Gao, Xin

doi:10.1016/j.eclinm.2022.101348

Cited by 81 publications

(61 citation statements)

References 35 publications

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“…Convolutional neural network (CNN) is a typical artificial neural network in deep learning. It has achieved the most advanced image and video recognition and segmentation performance ( Zhang et al, 2017 ; Singh et al, 2020 ; Cui et al, 2022 ). Radiomics and deep learning systems have been proved to have excellent performance in classifying, detecting, and segmenting lesions, and even predicting the risk of cancer on medical images ( Hosny et al, 2019 ; Zheng et al, 2020 ; Castillo et al, 2021 ; Liu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features

Huang

Wang

et al. 2022

Front. Pharmacol.

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Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC).Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients’ clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted.Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86–0.96) was higher than that of RM (0.82, 95% CI: 0.75–0.89) and DLM (0.90, 95% CI: 0.85–0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77–0.93) was also higher than that of RM (0.68, 95% CI: 0.52–0.84) and DLM (0.79, 95% CI: 0.67–0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models.Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

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Section: Introductionmentioning

confidence: 99%

PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features

Huang

Wang

et al. 2022

Front. Pharmacol.

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“…Second, to construct the current nomogram, radiological features taken from portal phase CT and some clinical signs are primarily used. [4][5][6][7] Nonetheless, these radiological features are not easily accessible to clinical practitioners, and the resulting Nomogram is di cult to develop for general use. The nomogram developed in this study was primarily based on common serological indicators and the cT stage of GC patients before treatment.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, radiological features derived from CT scans during the portal phase and some clinical markers are the key building blocks of the NAC prediction model. [4][5][6][7] These methods have limited use in clinical practice because they are too expensive and sophisticated to accurately predict NAC e cacy. In clinical work, peripheral blood indicators within the normal range are often disregarded by investigators but may have signi cant clinical implications.…”

Section: Introductionmentioning

confidence: 99%

A nomogram based on imaging features and serological indicators for predicting efficacy of neoadjuvant chemotherapy in gastric cancer

Zhou

Wang

Ding

et al. 2023

Preprint

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Background Neoadjuvant chemotherapy (NAC) has been proven to be a powerful therapeutic choice for the advanced gastric cancer. However, the overall response rate is only 20-40% and there is a lack of sensitive indicators to predict the efficacy of the therapy. In this study, we aimed to construct a nomogram to predict the efficacy of NAC for gastric cancer. Method The study comprised 60 gastric cancer patients who underwent NAC. Patients were classified into effective (TRG 0-2) and ineffective (TRG 3) groups based on the Tumor Regression Grade (TRG). Clinical data were compared between the two groups, and binary logistic regression analysis was used to screen the independent factors that could predict efficacy. Then, a new nomogram was created and validated. Result In this study, a total of 33 patients (33/60, 55%) were successfully treated with NAC. Platelet Distribution Width, Adenosine Deaminase, Urea, and clinical T-stage of tumor were independent factors for predicting the efficacy of NAC for gastric cancer treatment (P < 0.05). The consistency index of the constructed Nomogram was 0.923 (95% CI: 0.851-0.995). Conclusion The nomogram developed in this study has a high degree of clinical utility, calibration and discrimination, which can help clinicians accurately predict the efficacy of NAC for gastric cancer patients.

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“…Many studies focused on prediction of treatment response of patients. Cui et al [ 32 ] constructed a pretreatment venous-phase CT-based DLR nomogram that combined handcrafted features, DL features and remarkable clinicopathological factors to identify locally advanced GC patients with good response to NAC. The nomogram achieved better than the clinical model and the separate use of two features that were built for comparison, attaining C-index values of 0.829, 0.804, and 0.827 in its internal validation cohort and two external validation cohorts, respectively.…”

Section: Dlr For Gastric Cancermentioning

confidence: 99%

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

Wong¹,

Chan²,

Yan³

et al. 2022

World J Gastroenterol

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Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.

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A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study

Cited by 81 publications

References 35 publications

PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features

PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features

A nomogram based on imaging features and serological indicators for predicting efficacy of neoadjuvant chemotherapy in gastric cancer

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

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