Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Yang, Bin; Zhong, Jian; Zhong, Jing; Ma, Lifang; Li, Ang; Ji, Han; Zhou, Changsheng; Duan, Shaofeng; Wang, Qinggen; Zhu, Chengzhang; Tian, Jiahe; Zhang, Longjiang; Wang, Feng; Zhu, Hong; Lu, Guangming

doi:10.3389/fonc.2020.01042

Cited by 21 publications

(17 citation statements)

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“…The organic integration of big data technology and medical image-assisted diagnosis has promoted the emergence of a new imaging method, that is, radiomics [ 14 – 16 ]. By extracting massive features from images and capturing potential intra-tumour heterogeneity to predict treatment response, radiomics can effectively solve the problem of difficulty in quantitative evaluation of tumour heterogeneity and guide the formulation of personalised treatment plans [ 17 , 18 ]. Single or combined imaging radiomics features can be used to guide accurate diagnosis, establish potential prognostic models and propose effective treatment strategies [ 19 – 21 ].…”

Section: Introductionmentioning

confidence: 99%

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

Liu¹,

Zhang

Zheng³

et al. 2021

Arch Gynecol Obstet

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Purpose Our objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy–radiation therapy. Methods This retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy–radiation therapy on advanced cervical cancer (> IIb) was evaluated. Results The number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917. Conclusion The random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy–radiation therapy based on high-resolution T2WIs for advanced cervical cancer (> IIb).

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Section: Introductionmentioning

confidence: 99%

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

Liu¹,

Zhang

Zheng³

et al. 2021

Arch Gynecol Obstet

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“…PET_RS calculated by lasso regression could directly associate with PFS. Intratumoral heterogeneity of PET/CT has been proved to be a prognostic predictor for some malignancies these years (10,31,32). The radiomics features extracted from PET/CT images allowed us to assess intratumoral and metabolic heterogeneity quantitatively.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Wang

2021

Front. Oncol.

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ObjectiveTo investigate radiomics features extracted from PET and CT components of 18F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).MethodsA total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.ResultsIn univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.ConclusionPET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.

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“…Yang et al concluded that a radiomic nomogram based on PET/CT rad-core and clinicopathological features was able to predict the overall survival of NSCLC patients [108]. A study focused on prediction of prognosis after immunotherapy suggested that PET/CT-based radiomics signature could be used before the start of treatment to identify those most likely to benefit from immunotherapy for advanced NSCLC patients [109].…”

Section: Applications Of Functional Radiomic Features In Lung Cancermentioning

confidence: 99%

Structural and functional radiomics for lung cancer

Jochems

Ibrahim

et al. 2021

Eur J Nucl Med Mol Imaging

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Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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Development and Validation of a Radiomics Nomogram Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Cited by 21 publications

References 53 publications

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Structural and functional radiomics for lung cancer

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