Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

Ma, Xiaoling; Xia, Liming; Wan, Weijia; Zhou, Wen

doi:10.1007/s00330-022-09153-z

Cited by 22 publications

(24 citation statements)

References 43 publications

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“…Among them, 42 studies were not related to radiomics, 34 studies covered patients beyond lung adenocarcinoma, and the imaging modality of 1 study was not of interest (ultrasound). 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.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…Table 1 presents the basic characteristics for all 17 retrospective studies which were published between 2018 and 2022 [ 23 , 30 – 45 ]. Most of the studies (14/17, 82.4%) were derived from one center [ 30 – 39 , 41 , 42 , 44 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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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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“…20 Building upon the Transformer architecture, the Swim Transformer is a recent advancement in the field of computer vision, 21 incorporating shifted non-overlapping windows and self-attention calculations for robust feature extractions in clinical tasks. 22,23 In our study, we aimed to establish a Swim Transformer network using MR datasets from SR patients with early-stage HCC for MVI classification. Subsequently, we applied the network to obtain MVI risk probabilities in patients undergoing RFA, thereby facilitating prognostic analysis.…”

Section: Introductionmentioning

confidence: 99%

“…By incorporating the self‐attention mechanism, transformers can effectively capture relationships between image regions with a greater flexibility 20 . Building upon the Transformer architecture, the Swim Transformer is a recent advancement in the field of computer vision, 21 incorporating shifted non‐overlapping windows and self‐attention calculations for robust feature extractions in clinical tasks 22,23 …”

Section: Introductionmentioning

confidence: 99%

A Transformer‐Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation

Wang,

Song

et al. 2024

Liver International

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Background & AimsWe aimed to develop a Transformer‐based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA.MethodsA Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI‐based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180).ResultsOver 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence‐free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a‐fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02–2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40–5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40–3.29, p = .001) yielded a c‐index of .731 (bootstrapped 95% CI: .667–.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence.ConclusionsThe proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early‐stage HCC patients undergoing RFA.

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Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics

Guo,

Lu,

Song

et al. 2024

Cancer Medicine

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BackgroundThe presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next‐generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed.MethodsClinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort.ResultsTwenty‐four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN‐positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild‐type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780).ConclusionThis study suggests that the integration of genomic profiling and clinical features identifies early‐invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

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Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

Cited by 22 publications

References 43 publications

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

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

A Transformer‐Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics

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