Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study

Gong, Jing; Liu, Jiyu; Jiang, Yaojun; Sun, Xiaochun; Nie, Shengdong

doi:10.1002/mp.13237

Cited by 29 publications

(19 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction score of the weighted score fusion strategy was generated by systematically increasing the weighting factor from 0.1 to 0.9 applied to the prediction scores generated by the CT radiomics model (or 0.9 to 0.1 applied to the prediction scores generated by the clinical feature-based model). A similar score-level fusion method was used in our previously reported study ( 11 ).…”

Section: Methodsmentioning

confidence: 99%

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis

Gong

Shen

et al. 2022

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

ObjectivesTo establish and validate a machine learning-based CT radiomics model to predict metachronous liver metastasis (MLM) in patients with colorectal cancer.MethodsIn total, 323 patients were retrospectively recruited from two independent institutions to develop and evaluate the CT radiomics model. Then, 1288 radiomics features were extracted to decode the imaging phenotypes of colorectal cancer on CT images. The optimal radiomics features were selected using a recursive feature elimination selector configured by a support vector machine. To reduce the bias caused by an unbalanced dataset, the synthetic minority oversampling technique was applied to resample the minority samples in the datasets. Then, both radiomics and clinical features were used to train the multilayer perceptron classifier to develop two classification models. Finally, a score-level fusion model was developed to further improve the model performance.ResultsThe area under the curve (AUC) was 0.78 ± 0.07 for the tumour feature model and 0.79 ± 0.08 for the clinical feature model. The fusion model achieved the best performance, with AUCs of 0.79 ± 0.08 and 0.72 ± 0.07 in the internal and external validation cohorts.ConclusionsRadiomics models based on baseline colorectal contrast-enhanced CT have high potential for MLM prediction. The fusion model combining radiomics and clinical features can provide valuable biomarkers to identify patients with a high risk of colorectal liver metastases.

show abstract

Section: Methodsmentioning

confidence: 99%

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis

Gong

Shen

et al. 2022

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For weighting average strategy, we systematically increased the weighting factor of prediction score generated by DL based scheme from 0.1 to 0.9 (or 0.9-0.1 for the prediction score generated by radiomics feature based scheme) to compute the fusion prediction score. A similar method was applied in our previously reported literature (25).…”

Section: Methodsmentioning

confidence: 99%

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan

et al. 2020

Self Cite

View full text Add to dashboard Cite

For stage-I lung adenocarcinoma, the 5-years disease-free survival (DFS) rates of non-invasive adenocarcinoma (non-IA) is different with invasive adenocarcinoma (IA). This study aims to develop CT image based artificial intelligence (AI) schemes to classify between non-IA and IA nodules, and incorporate deep learning (DL) and radiomics features to improve the classification performance. We collect 373 surgical pathological confirmed ground-glass nodules (GGNs) from 323 patients in two centers. It involves 205 non-IA (including 107 adenocarcinoma in situ and 98 minimally invasive adenocarcinoma), and 168 IA. We first propose a recurrent residual convolutional neural network based on U-Net to segment the GGNs. Then, we build two schemes to classify between non-IA and IA namely, DL scheme and radiomics scheme, respectively. Third, to improve the classification performance, we fuse the prediction scores of two schemes by applying an information fusion method. Finally, we conduct an observer study to compare our scheme performance with two radiologists by testing on an independent dataset. Comparing with DL scheme and radiomics scheme (the area under a receiver operating characteristic curve (AUC): 0.83 ± 0.05, 0.87 ± 0.04), our new fusion scheme (AUC: 0.90 ± 0.03) significant improves the risk classification performance (p < 0.05). In a comparison with two radiologists, our new model yields higher accuracy of 80.3%. The kappa value for inter-radiologist agreement is 0.6. It demonstrates that applying AI method is an effective way to improve the invasiveness risk prediction performance of GGNs. In future, fusion of DL and radiomics features may have a potential to handle the classification task with limited dataset in medical imaging.

show abstract

“…Thus, an optimal fusion of quantitative image features computed from both the tumor and global parenchymal regions can further improve model performance in detecting suspicious breast tumors [49] and predicting the risk of lung cancer recurrence (Tables 2 and 3). Such a fusion approach can also be expanded to optimally combine imaging markers and genomic biomarkers to improve model performance in cancer risk prediction, tumor diagnosis, and prognosis assessment [37,56].…”

Section: Discussionmentioning

confidence: 99%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

Self Cite

View full text Add to dashboard Cite

In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

show abstract

Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study

Cited by 29 publications

References 37 publications

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan

Developing global image feature analysis models to predict cancer risk and prognosis

Contact Info

Product

Resources

About