Classification of malignant and benign liver tumors using a radiomics approach

Starmans, Martijn P.A.; Miclea, Razvan L.; Voort, Sebastian R. van der; Niessen, Wiro J.; Thomeer, Maarten; Klein, Stefan

doi:10.1117/12.2293609

Cited by 19 publications

(23 citation statements)

References 23 publications

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“…However, two studies used MP‐MRI, including Hectors et al, which first reported that radiomics features identified with MP‐MRI in HCC lesions were significantly different from the normal liver tissue. Additionally, Starmans et al used MP‐MRI to classify malignant and benign liver tumors. Our study further extends MP‐MRI into a new application for use in HCC patients.…”

Section: Discussionsupporting

confidence: 88%

Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI

Sun

Bai

Xia

et al. 2020

Magnetic Resonance Imaging

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Background In unresectable hepatocellular carcinoma (HCC), methods to predict patients at increased risk of progression are required. Purpose To investigate the feasibility of radiomics model in predicting early progression of unresectable HCC after transcatheter arterial chemoembolization (TACE) therapy using preoperative multiparametric magnetic resonance imaging (MP‐MRI). Study Type Retrospective. Population A total of 84 patients with BCLC B stage HCC from one medical center. According to the modified response evaluation criteria in solid tumors, patients who progressed at 6 months after TACE therapy were assigned as the progressive disease (PD) group (n = 32). Patients whose MRI was performed on four devices were divided into a training cohort (n = 67). Patients whose MRI was performed on other than the previous four devices were used as the testing set (n = 17). Field Strength/Sequence 3.0T, 1.5T axial T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI, b = 0, 500 s/mm2), and apparent diffusion coefficient (ADC) Assessment PD was confirmed via imaging studies with MRI. Risk factors, including age, alpha fetoprotein (AFP), size, and radiomic‐related features of PD were assessed. In addition, the discrimination ability of each radiomics signature was tested on an independent testing set. Statistical Tests The area under the receiver‐operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and testing sets. The results indicated that the MP‐MRI model achieved the greatest benefit. Results In the testing set, the model based on DWI features presented an AUC of (b = 0, 0.786; b = 500, 0.729), followed by T2WI features (0.729) and ADC (0.714). The AUC of the MP‐MRI signature was increased to 0.800 compared to any single MRI signature. The multivariate logistic analysis identified the radiomics signature as independent parameters of PD, while clinical information such as age, AFP, size, etc., had no significance in the PD group. Data Conclusion Preoperative MP‐MRI has the potential to predict the outcome of TACE therapy for unresectable HCC. In addition, these image features may be complementary to the current staging systems of HCC patients. Level of Evidence 2. Technical Efficacy Stage 3. J. Magn. Reson. Imaging 2020;52:1083–1090.

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Section: Discussionsupporting

confidence: 88%

Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI

Sun

Bai

Xia

et al. 2020

Magnetic Resonance Imaging

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“…In patients with >2 lung metastases of ≥10 mm, either the two largest or the two most easily distinguishable lesions were segmented (i.e., two separate lesions were preferred over two adjacent lesions). Using in-house developed software [ 29 ], selected lung metastases were segmented semi-automatically using a lung window for visualization. The result was visually inspected and manually corrected when necessary by an experienced chest radiologist to ensure that the semi-automatic segmentation resembled the manual segmentation.…”

Section: Methodsmentioning

confidence: 99%

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning

Angus

Starmans

Rajicic

et al. 2021

JPM

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Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.

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“…The lipoma and WDLPS lesions were segmented semiautomatically on the T1 images to indicate the regions of interest (ROIs). All images were segmented independently by either a medical masters student or a PhD candidate with an MD degree.…”

Section: Methodsmentioning

confidence: 99%

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI

Vos

Starmans

Timbergen

et al. 2019

British Journal of Surgery

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Background Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI. Methods Patients with an MDM2‐negative lipoma or MDM2‐positive WDLPS and a pretreatment T1‐weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random‐split cross‐validation. The performance of the models was compared with the performance of three expert radiologists. Results The data set included 116 tumours (58 patients with lipoma, 58 with WDLPS) and originated from 41 different MRI scanners, resulting in wide heterogeneity in imaging hardware and acquisition protocols. The radiomics model based on T1 imaging features alone resulted in a mean area under the curve (AUC) of 0·83, sensitivity of 0·68 and specificity of 0·84. Adding the T2‐weighted imaging features in an explorative analysis improved the model to a mean AUC of 0·89, sensitivity of 0·74 and specificity of 0·88. The three radiologists scored an AUC of 0·74 and 0·72 and 0·61 respectively; a sensitivity of 0·74, 0·91 and 0·64; and a specificity of 0·55, 0·36 and 0·59. Conclusion Radiomics is a promising, non‐invasive method for differentiating between WDLPS and lipoma, outperforming the scores of the radiologists. Further optimization and validation is needed before introduction into clinical practice.

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Classification of malignant and benign liver tumors using a radiomics approach

Cited by 19 publications

References 23 publications

Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI

Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI

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