Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status

Lenga, Lukas; Bernatz, Simon; Martin, Simon S.; Booz, Christian; Solbach, Christine; Mulert-Ernst, Rotraud; Vogl, Thomas J.; Leithner, Doris

doi:10.3390/cancers13102431

Cited by 27 publications

(24 citation statements)

References 36 publications

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“…This indicates that DESCT-based radiomics has great application prospects in gastric cancer. Previous studies have found that radiomic features derived from dual-energy CT iodine maps are useful in predicting breast cancer metastatic status [ 36 ] and identifying cervical lymph node metastases of thyroid cancer [ 37 ]. In this investigation, iodine-based MD images obtained from DESCT also performed well in predicting GC histologic grade, and the model’s performance was increased when paired with conventional polychromatic images.…”

Section: Discussionmentioning

confidence: 99%

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

Shi

Yan

et al. 2022

BMC Med Imaging

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Background The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades. Methods A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP–IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP–IMD and clinical features. The performance of these models was evaluated and compared. Results Model-CP–IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP–IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP–IMD and model-Combine outperformed model-CP according to decision curve analysis. Conclusion DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

Shi

Yan

et al. 2022

BMC Med Imaging

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“…Radiomics features represented tumor heterogeneity and were extracted from the entire ROI; they were not just limited to the biopsy site (17). Previous studies demonstrated that radiomics plays a role in differentiating between primary and metastatic tumors (25)(26)(27)(28)(29). In particular, CT radiomics features combined with positron emission tomography (PET) features can accurately distinguish between primary and metastatic lung cancers (26,27).…”

Section: Discussionmentioning

confidence: 99%

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

Zhong

Liu

et al. 2022

Front. Oncol.

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BackgroundThe objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs).MethodsThis retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images.ResultsA rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic–radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score.ConclusionThe proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.

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“…Previous studies (25)(26)(27) have demonstrated heterogeneity of tumor perfusion, especially for necrosis and microvessel density (MVD), which are related to aggressive tumor behavior and decreased survival rate, which in turn are prognostic factors for breast cancers. Also, Lenga et al (15) found that iodine map radiomics can serve as an additional tool for assessing the risk of metastasis in breast cancers. Different perfusion and permeability exist in the intratumorally regions of well-oxygenated tissue, hypoxia, or necrosis, which may be reflected by the heterogeneity of iodine maps.…”

Section: Discussionmentioning

confidence: 99%

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

Zeng¹,

Chen²,

Lin³

et al. 2022

Quant Imaging Med Surg

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Background: Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with earlystage breast cancer.Methods: Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test.Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.Results: This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).Conclusions: Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

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Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status

Cited by 27 publications

References 36 publications

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

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