Development of deep learning model for prediction of chemotherapy response using PET images and radiomics features

Kim, Wook; Park, Jisu; Sheen, Heesoon; Byun, Byung Hyun; Lim, Ilhan; Kong, Chang-Bae; Lim, Sang Moo; Woo, Sang-Keun

doi:10.1109/nssmic.2018.8824538

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…Instead, our contribution lies in the essence of that constructing more complex feature is necessary for selecting certain features which contribute to classification in the next step. Since not all features were effective, the information gain was further introduced as a standard to measure the correlation between features and ACI, which is according to the principle of feature distinction and independence in mathematical description [ 16 – 18 ]. Furthermore, machine learning is often used as a means to evaluate radiomic analysis [ 24 – 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…The information gain was further introduced as a statistical standard to measure the correlation between radiomic features and ACI, which was devoted to select significative information from a great deal features of above [ 16 – 18 ]. Each feature was calculated out a value in terms of information gain for dichotomy, lesion, or normal regions, by the equation below:

where X i represents the random variable of i th feature value, x ∈ X i denotes the possible value of random variable X i , p ( x ) represents the probability when the random variable X i takes the value x , and Y denotes the random variable of whether or not a cerebral infarction.…”

Section: Methodsmentioning

confidence: 99%

“…Theoretically, the feature was effective when the value was greater than zero, but we chose 0.1 as the minimal threshold to prevent calculating error and sampling error [ 18 ]. That is, features below the threshold were considered to be insignificant.…”

Section: Methodsmentioning

confidence: 99%

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Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis

Guan

Wang

et al. 2020

BioMed Research International

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This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled. Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions. MRI was reconstructed and affine transformed to obtain accurate lesion position of CT. Radiomic features and information gain were introduced to capture efficient features. Finally, 10 classifiers were established with selected features to evaluate the effectiveness of analysis. 1301 radiomic features were extracted from candidate regions after registration. For lesion and their symmetrical regions, there were 280 features with information gain greater than 0.1 and 2 features with information gain greater than 0.3. The average classification accuracy was 0.6467, and the best classification accuracy was 0.7748. For normal brain and their symmetrical regions, there were 176 features with information gain greater than 0.1, 1 feature with information gain greater than 0.2. The average classification accuracy was 0.5414, and the best classification accuracy was 0.6782. For normal brain and lesions, there were 501 features with information gain greater than 0.1 and 1 feature with information gain greater than 0.5. The average classification accuracy was 0.7480, and the best classification accuracy was 0.8694. In conclusion, the study captured significant features correlated with acute cerebral infarction and confirmed the separability of acute lesions in CT, which established foundation for further artificial intelligence-assisted CT diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis

Guan

Wang

et al. 2020

BioMed Research International

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“…DL methods are now robust enough to give a highly accurate prediction of EGFR mutations from histological images as well as radiological images (Mahajan et al, 2020; Wang et al, 2019; Zhao et al, 2019). Moreover, there is significant potential in the use of DL methods in identifying patients who respond to a given therapy (Kim et al, 2018; Xu et al, 2019) which has the potential of changing treatment decisions and avoiding un‐useful and needless treatment in patients who do not respond to therapy.…”

Section: Cancer Imaging Modalities and Goalsmentioning

confidence: 99%

A 2021 update on cancer image analytics with deep learning

Kurian

Sethi

Konduru

et al. 2021

WIREs Data Min & Knowl

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Deep learning (DL)‐based interpretation of medical images has reached a critical juncture of expanding outside research projects into translational ones, and is ready to make its way to the clinics. Advances over the last decade in data availability, DL techniques, as well as computing capabilities have accelerated this journey. Through this journey, today we have a better understanding of the challenges to and pitfalls of wider adoption of DL into clinical care, which, according to us, should and will drive the advances in this field in the next few years. The most important among these challenges are the lack of an appropriately digitized environment within healthcare institutions, the lack of adequate open and representative datasets on which DL algorithms can be trained and tested, and the lack of robustness of widely used DL training algorithms to certain pervasive pathological characteristics of medical images and repositories. In this review, we provide an overview of the role of imaging in oncology, the different techniques that are shaping the way DL algorithms are being made ready for clinical use, and also the problems that DL techniques still need to address before DL can find a home in clinics. Finally, we also provide a summary of how DL can potentially drive the adoption of digital pathology, vendor neutral archives, and picture archival and communication systems. We caution that the respective researchers may find the coverage of their own fields to be at a high‐level. This is so by design as this format is meant to only introduce those looking in from outside of deep learning and medical research, respectively, to gain an appreciation for the main concerns and limitations of these two fields instead of telling them something new about their own. This article is categorized under: Technologies > Artificial Intelligence Algorithmic Development > Biological Data Mining

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“…Therefore, the region level is analyzed first, and then, the region-level method is transferred to the pixel level for verification. To measure the effectiveness of features and classification models, information gain (IG) [19][20][21] and machine learning methods are introduced.…”

Section: Introductionmentioning

confidence: 99%

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

Chai

Qin

et al. 2021

BioMed Research International

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Objectives. To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods. In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. Results. Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. Conclusions. Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.

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Development of deep learning model for prediction of chemotherapy response using PET images and radiomics features

Cited by 5 publications

References 11 publications

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis

A 2021 update on cancer image analytics with deep learning

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

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