A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma

Liu, Yuanqing; Chang, Yue Cune; Zha, Xinyi; Bao, Jiayi; Wu, Qian; Dai, Hui; Hu, Chunhong

doi:10.1016/j.acra.2022.02.024

Cited by 9 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process enables the identi cation of additional factors that may not be readily discernible through visual inspection. Numerous previously published radiomics studies have investigated the signi cance of preoperative categorization based on the histological subtypes of LADC [15][16][19][20][21][22]. However, these studies included LADC patients, covering SPN and GGN types of lung nodules observed in CT scans.…”

Section: Discussionmentioning

confidence: 99%

“…However, these morphological characteristics lack representativeness, displaying a signi cant overlap in subtypes, and their performance remains inadequately evaluated, exhibiting considerable interobserver variability. The exploration of radiomic features extracted from CT images provides supplementary information intricately linked to MP/S components [15][16]. Despite this, current radiomics primarily extract characteristics of the entire tumor, disregarding intratumoral heterogeneity (ITH).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

Zuo,

Deng,

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: This study aims to quantify intratumoral heterogeneity(ITH) using preoperative CT scans and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC). Methods: In this retrospective study, 457 patients postoperatively diagnosed with clinical stage I solid LADC were included from two medical centers, comprising a training set (center 1, n=304) and a test set (center 2, n=153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined. Results: The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as indicated by higher AUC values in the training (0.820 versus 0.810 and 0.700) and test sets (0.805 versus 0.771 and 0.732), respectively. Notably, the hybrid model consistently demonstrated robust predictive capabilities in identifying MP/S (+), achieving AUCs of 0.830 in the training set and 0.849 in the test sets. Conclusion: The ITH of sub-regions within the intratumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC. This finding holds the potential to make a significant contribution to clinical decision-making processes.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

Zuo,

Deng,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Their combined model has proven highly effective, achieving impressive AUCs of 0.88 and 0.94 in the training and validation cohorts. 16 The research reveals the significant potential of employing a multifaceted approach for predictive purposes. It opens up new avenues for precise classification and diagnosis of lung adenocarcinoma in the future.…”

Section: Discussionmentioning

confidence: 99%

“… 14 The development and validation of this quantitative imaging technique could introduce a new, noninvasive, and convenient method for improving therapeutic strategy management, leading to enhanced clinical and economic benefits for the patient. 15 , 16 …”

Section: Introductionmentioning

confidence: 99%

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Xing,

Li,

Sun

et al. 2024

Ther Adv Respir Dis

View full text Add to dashboard Cite

Background: Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. Objective: This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. Design: A retrospective case control, diagnostic accuracy study. Methods: This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21–83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. Results: The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. Conclusion: This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

show abstract

“…Molecular biomarkers can provide information about the underlying biological mechanisms that drive tumor growth and progression, while radiomic markers can provide information about the resulting changes in tumor phenotype. By combining molecular and radiomic biomarkers, a more comprehensive understanding of tumor biology and accurate prognostic and predictive models could be developed [ 179 ].…”

Section: Radiomic Markersmentioning

confidence: 99%

Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases

2023

View full text Add to dashboard Cite

A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.

show abstract

A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma

Cited by 9 publications

References 35 publications

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases

Contact Info

Product

Resources

About