The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

She, Yunlang; Zhang, Lei; Zhu, Huanhuan; Dai, Chenyang; Xie, Dong; Xie, Huikang; Zhang, Wei; Zhao, Lilan; Zou, Liling; Fei, Ke; Sun, Xuejun; Chen, Chang

doi:10.1007/s00330-018-5509-9

Cited by 100 publications

(89 citation statements)

References 28 publications

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“…GLRLM‐Run Length Non‐uniformity assesses the distribution of runs over the run lengths. Radiomics can have objective reflections on both the attenuation and dispersion of gray level intensity through quantitative analysis for MR images, which may be less apparent in direct visual assessment . Although the best way to determine tumor heterogeneity is to detect molecular subtypes using tissue specimens, which taken by colonoscopy are only sufficient for pathological diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics can have objective reflections on both the attenuation and dispersion of gray level intensity through quantitative analysis for MR images, which may be less apparent in direct visual assessment. 31 Although the best way to determine tumor heterogeneity is to detect molecular subtypes using tissue specimens, which taken by colonoscopy are only sufficient for pathological diagnosis. Therefore, MRI-based radiomics analysis helps us to deepen the understanding of CRC disease, improve the diagnosis, and assessment therapy response after nCRT.…”

Section: Discussionmentioning

confidence: 99%

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Predicting pathological complete response by comparing MRI‐based radiomics pre‐ and postneoadjuvant radiotherapy for locally advanced rectal cancer

Liu

Pei

et al. 2019

Cancer Medicine

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BackgroundTotal mesorectal excision following neoadjuvant chemoradiotherapy (nCRT) is recommended in the latest treatment of locally advanced rectal cancer (LARC).ObjectiveTo predict whether patients with LARC can achieve pathologic complete response (pCR), comparing MRI‐based radiomics between before and after neoadjuvant radiotherapy (nRT) was performed.MethodsOne hundred and sixty‐five MRI‐based radiomics features in axial T2‐weighted images were obtained quantitatively from Imaging Biomarker Explorer Software. The specific features of conventional and developing radiomics were selected with the analysis of least absolute shrinkage and selection operator logistic regression, of which the predictive performance was analyzed with receiver operating curve and calibration curve, and applied to an independent cohort.ResultsOne hundred and thirty‐one target patients were enrolled in the present study. A radiomics signature founded on seven radiomics features was generated in the primary cohort. A remarkable difference about Rad‐score between pCR and non‐pCR group occurred in both of primary (P < .001) or validation cohorts (P < .001). The value of area under the curves was 0.92 (95% CI, 0.86‐0.99) and 0.87 (95% CI, 0.74‐1.00) in the primary and validation cohorts, respectively. The Rad‐score (OR = 23.581; P < .001) from multivariate logistic regression analysis was significant as an independent factor of pCR.ConclusionOur predictive model based on radiomics features was an independent predictor for pCR in LARC and could be a candidate in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting pathological complete response by comparing MRI‐based radiomics pre‐ and postneoadjuvant radiotherapy for locally advanced rectal cancer

Liu

Pei

et al. 2019

Cancer Medicine

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“…In addition, radiologist diagnosis utilized CT characteristics including contrast enhancement and more image features (pleural indentation, vacuole, vascular invasion) of the nodule, which the three models did not take into consideration for their risk classification. Finally, due to the lack of PET information and quantitative nodule features, we did not evaluate nodule classification models that incorporated PET information (31,32) and radiomics features (33)(34)(35), which might be more accurate than the classification models evaluated in our study.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Cui¹,

Heuvelmans²,

Han³

et al. 2019

Transl. Lung Cancer Res.

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Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population . (2019). Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population. Translational lung cancer research, 8(5), 605-613. Background: Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients. Methods: This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference. Results: In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models. Conclusions: In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable. of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population. Transl Lung Cancer Res 2019;8(5):605-613.

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“…None of the clinical parameters correlated with PFS or OS (Table 3). Texture analysis among other techniques of unsupervised feature extraction has been showing promising results in different oncologic settings (20,27) and more specifically in assessing prognosis or therapy response in lung cancer patients (28)(29)(30). In this study, a non-invasive texture-based signature was built using a machine learning method (31).…”

Section: Follow-up and Survival Assessmentmentioning

confidence: 99%

CT texture analysis as predictive factor in metastatic lung adenocarcinoma treated with tyrosine kinase inhibitors (TKIs)

Ravanelli

Agazzi

Ganeshan

et al. 2018

European Journal of Radiology

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Purpose: To assess the predictive and prognostic value of pre-treatment CT texture features in lung adenocarcinoma treated with tyrosine kinase inhibitors (TKI). Materials and Methods: Texture analysis was performed using commercially available software (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT studies from 50 patients with metastatic lung adenocarcinoma treated by TKI. Texture features were quantified on a 5-mm-thick central slice of the primary tumor and were correlated with progression-free and overall survival (PFS and OS) using an internally cross-validated machine learning approach then validated on a bootstrapped sample. Results: Median PFS and OS were 10.5 and 20.7 months, respectively. A noninvasive signature based on five texture parameters predicted 6-month progression with Area Under the Curve (AUC) of 0.8 (95%CI) and 1-year progression with AUC of 0.76. A high-risk group had hazard ratios for progression of 4.63 and 5.78 when divided by median and best cutoff points, respectively. Texture signature did not correlate with OS. Available clinical variables did not correlate with PFS or with OS. Conclusion: Texture features seem to be associated with PFS in lung adenocarcinoma treated with TKI.

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The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

Abstract: • Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.

Cited by 100 publications

References 28 publications

Predicting pathological complete response by comparing MRI‐based radiomics pre‐ and postneoadjuvant radiotherapy for locally advanced rectal cancer

Predicting pathological complete response by comparing MRI‐based radiomics pre‐ and postneoadjuvant radiotherapy for locally advanced rectal cancer

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

CT texture analysis as predictive factor in metastatic lung adenocarcinoma treated with tyrosine kinase inhibitors (TKIs)

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