A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Zhang, Ran; Ran-ran, Zhang; Luan, Ting; Liu, Biwei; Zhang, Yimei; Xu, Yaping; Sun, Xuejun; Xing, Ligang

doi:10.2147/cmar.s330824

Cited by 9 publications

(28 citation statements)

References 44 publications

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“…However, multivariate logistic regression analysis revealed that CEA level was not an OLNM risk factor in our research, which is consistent with the results of Zhang et al. ( 17 ). In addition, PET/CT was proclaimed to be useful in predicting OLNM in patients with early-stage T adenocarcinoma or squamous cell carcinoma ( 29 ), Matsushima et al.…”

Section: Discussionsupporting

confidence: 93%

“…Wang et al revealed that CEA >5.00 ng/ml was a risk indicator for OLNM, with a sensitivity and specificity of 68.32% and 78.55%, respectively (28). However, multivariate logistic regression analysis revealed that CEA level was not an OLNM risk factor in our research, which is consistent with the results of Zhang et al (17). In addition, PET/CT was proclaimed to be useful in predicting OLNM in patients with early-stage T adenocarcinoma or squamous cell carcinoma (29), Matsushima et al revealed that FDG SUVmax≥5 was a reliable risk predictor of lymph node metastasis upstage (30).…”

Section: Discussionsupporting

confidence: 90%

“…Although patients with an adenocarcinoma tumor lesion diameter ≤ 3cm have a favorable prognosis, OLNM could still develop in a few individuals. Recent researches indicate that OLNM is prevalent in individuals with IAC, especially in patients with peripheral solid NSCLC, with an incidence of approximately 16.2-25% ( 16 , 17 ). Especially since the new IAC pathological classification has been introduced, there are numerous reports that patients with a predominance of solid and micropapillary often present with poorer prognosis, have a pleural invasion, and present with intrapulmonary or lymphatic system metastases ( 18 , 19 ).…”

Section: Discussionmentioning

confidence: 99%

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Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

et al. 2022

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BackgroundThe efficacy of sublobar resection and selective lymph node dissection is gradually being accepted by thoracic surgeons for patients within early-stage non-small cell lung cancer (NSCLC). Nevertheless, there are still some NSCLC patients develop lymphatic metastasis at clinical T1 stage. Lung adenocarcinoma with a micropapillary (MP) component poses a higher risk of lymph node metastasis and recurrence even when the MP component is not predominant. Our study aimed to explore the genetic features and occult lymph node metastasis (OLNM) risk factors in patients with a non-predominant micropapillary component (NP-MPC) in a large of patient’s cohort with surgically resected lung adenocarcinoma.MethodsBetween January 2019 and December 2021, 6418 patients who underwent complete resection for primary lung adenocarcinoma at the Qilu Hospital of Shandong University. In our study, 442 patients diagnosed with lung adenocarcinoma with NP-MPC with a tumor size ≤3 cm were included. Genetic alterations were analyzed using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Abnormal protein expression of gene mutations was validated using immunohistochemistry. A nomogram risk model based on clinicopathological parameters was developed to predict OLNM. This model was invalidated using the calibration plot and concordance index.ResultsIn our retrospective cohort, the incidence rate of the micropapillary component was 11.17%, and OLNM was observed in 20.13% of the patients in our study. ARMS-PCR suggested that EGFR exon 19 del was the most frequent alteration in NP-MCP patients compared with other gene mutations (frequency: 21.2%, P<0.001). Patients harboring exon 19 del showed significantly higher risk of OLNM (P< 0.001). A nomogram was developed based on five risk parameters, which showed good calibration and reliable discrimination ability (C-index = 0.84) for evaluating OLNM risk.Conclusions.Intense expression of EGFR exon 19 del characterizes lung adenocarcinoma in patients with NP-MCP and it’s a potential risk factor for OLNM. We firstly established a nomogram based on age, CYFRA21-1 level, tumor size, micropapillary and solid composition, that was effective in predicting OLNM among NP-MCP of lung adenocarcinoma measuring ≤ 3 cm.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

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Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

et al. 2022

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“…Finally, this systematic review involved 17 studies containing a total of 7,117 patients [ 23 , 30 – 45 ]. Seven studies [ 30 , 31 , 35 , 37 – 39 , 44 ] were excluded due to lack of sufficient data, and 10 studies [ 23 , 32 – 34 , 36 , 40 – 43 , 45 ] were included in the meta-analysis. Figure 1 illustrates the PRISMA flow chart for the included studies in this review.…”

Section: Resultsmentioning

confidence: 99%

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

Wu,

Gao,

Lou

et al. 2024

BMC Pulm Med

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Background The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. Methods Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. Results Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73–0.91]), 0.88 (95% CI [0.81–0.93]), and 0.93(95% CI [0.90–0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82–0.94]), 0.86 (95% CI [0.74–0.93]) and 0.94 (95% CI [0.91–0.96]), respectively. Conclusions Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. Trial registration CRD42022375712.

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“…This approach enables the analysis of high-level and quantitative image features, providing a profound re ection of the spatial heterogeneity within tumour tissues (21). Previous studies have successfully developed models for predicting OLM in NSCLC patients based on radiomic features of primary lesions, demonstrating robust predictive performance (22)(23)(24). Additionally, peritumoral radiomics has proven equally predictive (25,26).…”

Section: Introductionmentioning

confidence: 99%

Incidence Rate of Occult Lymph Node Metastasis in Clinical T 1-2 N 0 M 0 Small Cell Lung Cancer Patients and Radiomic Prediction Based on Contrast-enhanced CT Imaging: A Multicentre Study

Jiang,

Luo,

Peng

et al. 2024

Preprint

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Background This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 − 2N0M0 (cT1 − 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. Methods By conducting a retrospective analysis involving 242 eligible patients from 4 centres, we determined the incidence of OLM in cT1 − 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). Results The initial investigation revealed a 33.9% OLM positivity rate in cT1 − 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for T1 − 2N0M0 SCLC patients. Conclusions The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 − 2N0M0 SCLC.

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A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Cited by 9 publications

References 44 publications

Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

Incidence Rate of Occult Lymph Node Metastasis in Clinical T 1-2 N 0 M 0 Small Cell Lung Cancer Patients and Radiomic Prediction Based on Contrast-enhanced CT Imaging: A Multicentre Study

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