Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

Dong, Hao; Yin, Lina; Qiu, Yong-Gang; Wang, Xin-Bin; Yang, Junjie; Lou, Cun-Cheng; Ye, Xiaodan

doi:10.1007/s00330-022-09379-x

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Several previous studies have utilized Clin-Rad features to construct models for predicting MP/S (+) from MP/S (-) in clinical stage I solid LADC. In a study conducted by Dong et al [14], tumor size, density, and lobulation were identi ed as risk factors for predicting MP/S (+). However, in this current study, CT radiological features such as spiculation, shape, boundary, lobulation, vascular convergence signs, and vacuole signs did not show signi cant associations in differentiating MP/S (+) from MP/S (-) in clinical stage I solid LADC.…”

Section: Discussionmentioning

confidence: 99%

“…Subgroup analyses for SPN or GGN types were not conducted. Due to the high heterogeneity and invasiveness, the MP/S (+) pattern more commonly presents as SPN in CT images [13][14]. This observation leads us to infer that a higher frequency of SPN in the enrolled population could signi cantly improve the diagnostic e ciency of radiomics, potentially resulting in model over tting.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast to ground glass nodules (GGN), solid pulmonary nodules (SPN) demonstrate greater invasiveness and heterogeneity. Accordingly, LADC patients with MP/S components more frequently present as SPN in CT images [13][14]. Within our investigation framework, we hypothesized that ITH within subregions of intratumor derived from CT images would exhibit a stronger correlation with MP/S components than that derived from the conventional whole tumor.…”

mentioning

confidence: 98%

“…Numerous studies have highlighted clinical-radiological (Clin-Rad) features that predict the presence of MP/S components through the analysis of CT images and clinical baseline data [13][14]. However, these morphological characteristics lack representativeness, displaying a signi cant overlap in subtypes, and their performance remains inadequately evaluated, exhibiting considerable interobserver variability.…”

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confidence: 99%

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

Zuo,

Deng,

et al. 2024

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 98%

mentioning

confidence: 99%

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

Zuo,

Deng,

et al. 2024

Preprint

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“…Studies on patients undergoing surgical resection have shown that over 95% of subsolid nodules are malignant, whereas the malignant rate of solid nodules ranges from 51% to 67% ( 4 – 6 ). In addition, the malignant degree of solid lung cancers is higher than that of subsolid ones, which are not suitable for long-term follow-up ( 1 , 2 , 7 ). Therefore, the accurate and timely diagnosis of solid nodules must be improved.…”

Section: Introductionmentioning

confidence: 99%

The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules

Zhang,

Zhou,

et al. 2024

Front. Oncol.

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ObjectivesThis study aimed to explore the value of radiomics nomogram based on computed tomography (CT) on the diagnosis of benign and malignant solitary indeterminate smoothly marginated solid pulmonary nodules (SMSPNs).MethodsThis study retrospectively reviewed 205 cases with solitary indeterminate SMSPNs on CT, including 112 cases of benign nodules and 93 cases of malignant nodules. They were divided into training (n=143) and validation (n=62) cohorts based on different CT scanners. Radiomics features of the nodules were extracted from the lung window CT images. The variance threshold method, SelectKBest, and least absolute shrinkage and selection operator were used to select the key radiomics features to construct the rad-score. Through multivariate logistic regression analysis, a nomogram was built by combining rad-score, clinical factors, and CT features. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC).ResultsA total of 19 radiomics features were selected to construct the rad-score, and the nomogram was constructed by the rad-score, one clinical factor (history of malignant tumor), and three CT features (including calcification, pleural retraction, and lobulation). The nomogram performed better than the radiomics model, clinical model, and experienced radiologists who specialized in thoracic radiology for nodule diagnosis. The AUC values of the nomogram were 0.942 in the training cohort and 0.933 in the validation cohort. The calibration curve and decision curve showed that the nomogram demonstrated good consistency and clinical applicability.ConclusionThe CT-based radiomics nomogram achieved high efficiency in the preoperative diagnosis of solitary indeterminate SMSPNs, and it is of great significance in guiding clinical decision-making.

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Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography

Zhang,

Sha,

Liu

et al. 2024

Academic Radiology

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Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

Cited by 5 publications

References 36 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

The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography

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