Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Ji, Yanlei; Qiu, Qingtao; Fu, Jing; Cui, Kai; Chen, Xia; Xing, Ligang; Sun, Xuejun

doi:10.2147/cmar.s287128

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…Although radiomics has not been widely used in the clinic, many studies have shown its excellent performance in tumor diagnosis and efficacy prediction. 49 , 50 As multidisciplinary overlap is an inexorable trend in modern medicine, we expect the widespread adoption of a standardized radiomic procedure.…”

Section: Discussionmentioning

confidence: 99%

Different Clinicopathologic and Computed Tomography Imaging Characteristics of Primary and Acquired EGFR T790M Mutations in Patients with Non-Small-Cell Lung Cancer

Hou¹,

Wang³

et al. 2021

CMAR

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Purpose Although patients with primary and acquired epidermal growth factor receptor (EGFR) T790M positive non-small-cell lung cancer (NSCLC) respond to osimertinib treatment, the optimal treatment strategy differs for these two groups of patients. This study aimed to compare the clinicopathologic and computed tomography (CT) imaging characteristics between primary and acquired EGFR T790M mutations in patients with NSCLC before treatment. Patients and Methods We enrolled two groups of patients with primary or acquired EGFR T790M mutation NSCLC (n = 103 per group) from January 2012 to December 2019. We analyzed their clinicopathologic and CT characteristics and differences between the groups. The groups were further categorized based on 21L858R and 19del to exclude the effect of coexistent mutations. Results Primary, compared to acquired, T790M mutation tends to coexist with 21L858R (P < 0.001), exhibiting earlier tumor stage (P < 0.001), higher differentiation (P = 0.029), higher proportion of lepidic subtype adenocarcinoma (P < 0.001), and significant associations with some CT features (multiple primary lung cancers, ground-glass opacity, air bronchogram, and vacuole sign [all P < 0.001]). The combined model, composed of clinicopathologic and conventional CT signature and CT-radiomic signature, showed good discriminative ability with the area under the receiver operating characteristic curve 0.90 and 0.91 in the training and validation datasets, respectively. The T790M mutation contributed to these differences independently of coexistent mutations. Conclusion We identified clinicopathologic and CT imaging differences between primary and acquired T790M mutations. These findings provide insights into developing future personalized T790M mutation status-based theranostic strategies.

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Section: Discussionmentioning

confidence: 99%

Different Clinicopathologic and Computed Tomography Imaging Characteristics of Primary and Acquired EGFR T790M Mutations in Patients with Non-Small-Cell Lung Cancer

Hou¹,

Wang³

et al. 2021

CMAR

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“…To make the model developed more predictive and robust, feature selection is a crucial step of a radiomics study ( 27 ). The Rad-Score consisted of eight features, and nearly all of the selected predictive features were wavelet, similar to the results of several published studies ( 28 - 30 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Cong¹,

Qiu²,

Li³

et al. 2021

Transl Cancer Res TCR

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Background: To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).Methods: One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra-and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.Results: A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI), 0.903-0.919)] and an AP of 0.885 (95% CI, 0.876-0.894) in the training cohort, and an AUC of 0.873 (95% CI, 0.866-0.80) and an AP of 0.827 (95% CI, 0.820-0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427). Conclusions:The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.

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“…Since the two different types of lung cancer have different prognoses, an accurate pre-operative diagnosis will lead to appropriate treatment and will improve patient prognosis. Several studies have shown that radiomic analysis could help to classify the histological lung cancer subtypes shown in Table 3 [21,[65][66][67][68][69][70][71][72][73][74][75][76][77][78]. Lu et al reported that radiomics models yielded the diagnostic performance (AUC) of 0.741 (SCLC vs. NSCLC), 0.822 (AD vs. SCLC), 0.665 (SCC vs. SCLC), and 0.655 (AD vs. SCC) in the classification of histopathologic lung cancer subtypes [71].…”

Section: Application In Classifying Histological Subtypes Of Early Lu...mentioning

confidence: 99%

“…Therefore, it is necessary to cross-validate data using multi-center cohort data in the future. [70] 349 CT SPN Yes (internal) No Accuracy = 89% Yan lei Ji [72] 253 CT SPN Yes (internal) Yes AUC = 0.982 (ADC vs. SCC) Jianyuan ZHOU [78] 182 PET-CT SPN Yes (internal) Yes AUC = 0.862 (Sensitivity = 88%, Specificity = 72.73%, ADC vs. SCC) Xue Sha [69] 100 PET-CT SPN Yes (internal) Yes AUC = 0.781 (Sensitivity = 100%, Specificity = 70%, ADC vs. SCC) Weimiao Wu [66] 350 CT SPN Yes (internal) Yes AUC = 0.72 (ADC vs. SCC) ADC: adenocarcinoma; SCLC: small cell lung cancer; NSCLC: non-small cell lung cancer; SCC: squamous cell carcinoma. a Group: refers to the type of lung nodules analyzed in this study.…”

Section: Application In Classifying Histological Subtypes Of Early Lu...mentioning

confidence: 99%

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

Yang

et al. 2022

Diagnostics

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Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making.

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Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Cited by 10 publications

References 40 publications

Different Clinicopathologic and Computed Tomography Imaging Characteristics of Primary and Acquired EGFR T790M Mutations in Patients with Non-Small-Cell Lung Cancer

Different Clinicopathologic and Computed Tomography Imaging Characteristics of Primary and Acquired EGFR T790M Mutations in Patients with Non-Small-Cell Lung Cancer

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education

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