2021
DOI: 10.1007/s00330-021-08221-0
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Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study

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Cited by 35 publications
(25 citation statements)
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“…Deep learning model with CNN can learn the features of CT images from low to high dimensions and their correlation ( Yamashita et al, 2018 ), which may be the key reason for high performance in image analysis. Since most previous CT image based prognostic research have only used pretrained deep learning to extract images features, subsequent analysis required subjective screening of these features to build the machine learning model again ( Huang et al, 2020 ; Park et al, 2021 ; Liu et al, 2022 ). Besides, they did not consider the special characteristics of medical images which mean generic pretrained deep learning models were not suitable.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Deep learning model with CNN can learn the features of CT images from low to high dimensions and their correlation ( Yamashita et al, 2018 ), which may be the key reason for high performance in image analysis. Since most previous CT image based prognostic research have only used pretrained deep learning to extract images features, subsequent analysis required subjective screening of these features to build the machine learning model again ( Huang et al, 2020 ; Park et al, 2021 ; Liu et al, 2022 ). Besides, they did not consider the special characteristics of medical images which mean generic pretrained deep learning models were not suitable.…”
Section: Discussionmentioning
confidence: 99%
“…Deep learning, one kind of machine learning based on artificial neural networks, has a powerful ability in image analysis ( LeCun et al, 2015 ) with convolutional neural networks (CNNs). A few studies based on deep learning have proved its effectiveness in tumor assessment like lymph node status prediction ( Zheng et al, 2020 ) and tumor recurrence prediction ( Liu et al, 2022 ). Though with high prediction accuracy, deep learning is known as a black box as lacks the interpretation for its prediction, which makes it hard to be accepted by doctors.…”
Section: Introductionmentioning
confidence: 99%
“…Radiomics relies on algorithm‐based automated data characterization to extract large amounts of image features from radiographic images 12,13 . In recent years, 14–16 radiomics methods have been used to identify benign and malignant soft tissue tumors, distinguish middle and high‐grade sarcomas 17 and predict recurrence or metastasis of the soft tissue sarcomas 18,19 …”
Section: Introductionmentioning
confidence: 99%
“…Radiomics extracts numerous and quantitative information from medical images, including CT, MRI and PET, with high throughput to facilitate clinical decision-making [ 9 ]. The goals of radiomics are to improve decision support and reliability of prediction inexpensively and non-invasively [ 10 ]. Non-invasiveness is very crucial for pediatric patients.…”
Section: Introductionmentioning
confidence: 99%