Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology

Forghani, Reza; Savadjiev, Peter; Chatterjee, A.; Muthukrishnan, N. Moorthy; Reinhold, Caroline; Forghani, Behzad

doi:10.1016/j.csbj.2019.07.001

Cited by 144 publications

(127 citation statements)

References 81 publications

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“…Few results are finally applied in clinical practice yet due to the complicated procedure (e.g., time consuming, poor reproducibility, remaining the operator-dependency that is not biases-free, and so on) of Abbreviations: EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung carcinoma; PFS, progression-free survival; ALK, antigen anaplastic lymphoma kinase; ROS1, c-ros oncogene 1; TKIs, tyrosine kinase inhibitors. radiomic researches (16). In view of this, models that giving an individual numerical probability of a clinical event (e.g., nomogram) rather than a predicting accuracy, may be more suitable and convenient for clinical application.…”

Section: Introductionmentioning

confidence: 99%

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

Zhao

et al. 2020

Front. Oncol.

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Up to 50% of Asian patients with NSCLC have EGFR gene mutations, indicating that selecting eligible patients for EGFR-TKIs treatments is clinically important. The aim of the study is to develop and validate radiomics-based nomograms, integrating radiomics, CT features and clinical characteristics, to non-invasively predict EGFR mutation status and subtypes. Materials and Methods: We included 637 patients with lung adenocarcinomas, who performed the EGFR mutations analysis in the current study. The whole dataset was randomly split into a training dataset (n = 322) and validation dataset (n = 315). A sub-dataset of EGFR-mutant lesions (EGFR mutation in exon 19 and in exon 21) was used to explore the capability of radiomic features for predicting EGFR mutation subtypes. Four hundred seventy-five radiomic features were extracted and a radiomics sore (R-score) was constructed by using the least absolute shrinkage and selection operator (LASSO) regression in the training dataset. A radiomics-based nomogram, incorporating clinical characteristics, CT features and R-score was developed in the training dataset and evaluated in the validation dataset. Results: The constructed R-scores achieved promising performance on predicting EGFR mutation status and subtypes, with AUCs of 0.694 and 0.708 in two validation datasets, respectively. Moreover, the constructed radiomics-based nomograms excelled the R-scores, clinical, CT features alone in terms of predicting EGFR mutation status and subtypes, with AUCs of 0.734 and 0.757 in two validation datasets, respectively. Conclusions: Radiomics-based nomogram, incorporating clinical characteristics, CT features and radiomic features, can non-invasively and efficiently predict the EGFR mutation status and thus potentially fulfill the ultimate purpose of precision medicine. The methodology is a possible promising strategy to predict EGFR mutation subtypes, providing the support of clinical treatment scenario.

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Section: Introductionmentioning

confidence: 99%

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

Zhao

et al. 2020

Front. Oncol.

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“…There are multiple studies suggesting the CT imaging features extracted by CNNs have high predictive values in oncological outcomes [8,9]. Machine learning approach is one of the major subfields of artificial intelligence which can be used for constructing prediction model in radiomics [6] and has shown promising performances for predicting various oncological subjects [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan

et al. 2020

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Background Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related entity, but not on the clinical implication of the feature itself. Here we analyzed liver imaging features of abdominal CT images collected from 2019 patients with stage I – III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clinical implication in oncological perspectives. Results CNN generated imaging features from the liver parenchyma. Dimension reduction was done for the features by principal component analysis. We designed multiple prediction models for 5-year metachronous liver metastasis (5YLM) using combinations of clinical variables (age, sex, T stage, N stage) and top principal components (PCs), with logistic regression classification. The model using “1st PC (PC1) + clinical information” had the highest performance (mean AUC = 0.747) to predict 5YLM, compared to the model with clinical features alone (mean AUC = 0.709). The PC1 was independently associated with 5YLM in multivariate analysis (beta = − 3.831, P < 0.001). For the 5-year mortality rate, PC1 did not contribute to an improvement to the model with clinical features alone. For the PC1, Kaplan-Meier plots showed a significant difference between PC1 low vs. high group. The 5YLM-free survival of low PC1 was 89.6% and the high PC1 was 95.9%. In addition, PC1 had a significant correlation with sex, body mass index, alcohol consumption, and fatty liver status. Conclusion The imaging features combined with clinical information improved the performance compared to the standardized prediction model using only clinical information. The liver imaging features generated by CNN may have the potential to predict liver metastasis. These results suggest that even though there were no liver metastasis during the primary colectomy, the features of liver imaging can impose characteristics that could be predictive for metachronous liver metastasis.

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“…This approach fills the gap in the clinical use of information and extracts and analyzes higher-dimensional and quantitative data to more accurately and more specifically describe and characterize tumors. The use of ultrasomics to improve disease diagnosis and care for patients shows great potential (45). In the future, we hope that ultrasomics will provide a more personalized, higher-quality, and more cost-effective care platform for patients.…”

Section: Discussionmentioning

confidence: 99%

Study Processes and Applications of Ultrasomics in Precision Medicine

Yin

Jiang

Lv³

et al. 2020

Front. Oncol.

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Ultrasomics is the science of transforming digitally encrypted medical ultrasound images that hold information related to tumor pathophysiology into mineable high-dimensional data. Ultrasomics data have the potential to uncover disease characteristics that are not found with the naked eye. The task of ultrasomics is to quantify the state of diseases using distinctive imaging algorithms and thereby provide valuable information for personalized medicine. Ultrasomics is a powerful tool in oncology but can also be applied to other medical problems for which a disease is imaged. To date there is no comprehensive review focusing on ultrasomics. Here, we describe how ultrasomics works and its capability in diagnosing disease in different organs, including breast, liver, and thyroid. Its pitfalls, challenges and opportunities are also discussed.

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Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology

Abstract: Graphical abstract

Cited by 144 publications

References 81 publications

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan

Study Processes and Applications of Ultrasomics in Precision Medicine

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