Radiomics-based Management of Indeterminate Lung Nodules? Are We There Yet?
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ABSTRACT:
SUBMITTER: Peikert T
PROVIDER: S-EPMC7365373 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Radiomics-based Management of Indeterminate Lung Nodules? Are We There Yet?
Peikert Tobias T Bartholmai Brian J BJ Maldonado Fabien F
American journal of respiratory and critical care medicine 20200701 2
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Project description:PurposeAs ~25% of cytologically indeterminate thyroid nodules harbour malignancy, diagnostic lobectomy is still performed in many cases. 18FDG PET/CT rules out malignancy in visually negative nodules; however, none of the currently available interpretation criteria differentiates malignant from benign 18FDG-avid nodules. We evaluated the ability of PET metrics and radiomics features (RFs) to predict final diagnosis of 18FDG-avid cytologically indeterminate thyroid nodules.MethodsSeventy-eight patients were retrospectively included. After volumetric segmentation of each thyroid lesion, 4 PET metrics and 107 RFs were extracted. A logistic regression was performed including thyroid stimulating hormone, PET metrics, and RFs to assess their predictive performance. A linear combination of the resulting parameters generated a radiomics score (RS) that was matched with cytology classes (Bethesda III and IV) and compared with final diagnosis.ResultsTwo RFs (shape_Sphericity and glcm_Autocorrelation) differentiated malignant from benign lesions. A predictive model integrating RS and cytology classes effectively stratified the risk of malignancy. The prevalence of thyroid cancer increased from 5 to 37% and 79% in accordance with the number (score 0, 1 or 2, respectively) of positive biomarkers.ConclusionsOur multiparametric model may be useful for reducing the number of diagnostic lobectomies with advantages in terms of costs and quality of life for patients.
| S-EPMC8763930 | biostudies-literature
Project description:Evaluation of indeterminate pulmonary nodules is a complex challenge. Most are benign but frequently undergo invasive and costly procedures to rule out malignancy. A plasma protein classifier was developed that identifies likely benign nodules that can be triaged to CT surveillance to avoid unnecessary invasive procedures. The clinical utility of this classifier was assessed in a prospective-retrospective analysis of a study enrolling 475 patients with nodules 8-30 mm in diameter who had an invasive procedure to confirm diagnosis at 12 sites. Using this classifier, 32.0 % (CI 19.5-46.7) of surgeries and 31.8 % (CI 20.9-44.4) of invasive procedures (biopsy and/or surgery) on benign nodules could have been avoided. Patients with malignancy triaged to CT surveillance by the classifier would have been 24.0 % (CI 19.2-29.4). This rate is similar to that described in clinical practices (24.5 % CI 16.2-34.4). This study demonstrates the clinical utility of a non-invasive blood test for pulmonary nodules.
| S-EPMC4651976 | biostudies-literature
Project description:PurposeTo evaluate whether quantitative [18F]FDG-PET/CT assessment, including radiomic analysis of [18F]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hürthle cell and Hürthle cell cytology.MethodsProspectively included patients with a Bethesda III or IV thyroid nodule underwent [18F]FDG-PET/CT imaging. Receiver operating characteristic (ROC) curve analysis was performed for standardised uptake values (SUV) and SUV-ratios, including assessment of SUV cut-offs at which a malignant/borderline neoplasm was reliably ruled out (≥ 95% sensitivity). [18F]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [18F]FDG-PET and low-dose CT images. Elastic net regression classifiers were trained in a 20-times repeated random split. Dimensionality reduction was incorporated into the splits. Predictive performance of radiomics was presented as mean area under the ROC curve (AUC) across the test sets.ResultsOf 123 included patients, 84 (68%) index nodules were visually [18F]FDG-positive. The malignant/borderline rate was 27% (33/123). SUV-metrices showed AUCs ranging from 0.705 (95% CI, 0.601-0.810) to 0.729 (0.633-0.824), 0.708 (0.580-0.835) to 0.757 (0.650-0.864), and 0.533 (0.320-0.747) to 0.700 (0.502-0.898) in all (n = 123), non-Hürthle (n = 94), and Hürthle cell (n = 29) nodules, respectively. At SUVmax, SUVpeak, SUVmax-ratio, and SUVpeak-ratio cut-offs of 2.1 g/mL, 1.6 g/mL, 1.2, and 0.9, respectively, sensitivity of [18F]FDG-PET/CT was 95.8% (95% CI, 78.9-99.9%) in non-Hürthle cell nodules. In Hürthle cell nodules, cut-offs of 5.2 g/mL, 4.7 g/mL, 3.4, and 2.8, respectively, resulted in 100% sensitivity (95% CI, 66.4-100%). Radiomic analysis of 84 (68%) [18F]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290-0.600) for the PET model.ConclusionQuantitative [18F]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hürthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [18F]FDG-positive nodules.Trial registration numberThis trial is registered with ClinicalTrials.gov: NCT02208544 (5 August 2014), https://clinicaltrials.gov/ct2/show/NCT02208544 .
| S-EPMC9165273 | biostudies-literature
Project description:BackgroundRadiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.MethodsA total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above.ResultsIn the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05).ConclusionsThe present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.
| S-EPMC10883857 | biostudies-literature
Project description:Purpose18F-FDG PET/CT is widely used to evaluate indeterminate pulmonary nodules (IPNs). False positive results occur, especially from active granulomatous nodules. A PET-based imaging agent with superior specificity to 18F-FDG for IPNs, is badly needed, especially in areas of endemic granulomatous nodules. Somatostatin receptors (SSTR) are expressed in many malignant cells including small cell and non-small cell lung cancers (NSCLCs). 68Ga-DOTATATE, a positron emitter labeled somatostatin analog, combined with PET/CT imaging, may improve the diagnosis of IPNs over 18F-FDG by reducing false positives. Our study purpose was to test this hypothesis in our region with high endemic granulomatous IPNs.MethodsWe prospectively performed 68Ga-DOTATATE PET/CT and 18F-FDG PET/CT scans in the same 30 patients with newly diagnosed, treatment-naïve lung cancer (N = 14) or IPNs (N = 15) and one metastatic nodule. 68Ga-DOTATATE SUVmax levels at or above 1.5 were considered likely malignant. We analyzed the scan results, correlating with ultimate diagnosis via biopsy or 2-year chest CT follow-up. We also correlated 68Ga-DOTATATE uptake with immunohistochemical (IHC) staining for SSTR subtype 2A (SSTR2A) in pathological specimens.ResultsWe analyzed 31 lesions in 30 individuals, with 14 (45%) being non-neuroendocrine lung cancers and 1 (3%) being metastatic disease. McNemar's result comparing the two radiopharmaceuticals (p = 0.65) indicates that their accuracy of diagnosis in this indication are equivalent. 68Ga-DOTATATE was more specific (94% compared to 81%) and less sensitive 73% compared to 93%) than 18F-FDG. 68Ga-DOTATATE uptake correlated with SSTR2A expression in tumor stroma determined by immunohistochemical (IHC) staining in 5 of 9 (55%) NSCLCs.Conclusion68Ga-DOTATATE and 18F-FDG PET/CT had equivalent accuracy in the diagnosis of non-neuroendocrine lung cancer and 68Ga-DOTATATE was more specific than 18F-FDG for the diagnosis of IPNs. IHC staining for SSTR2A receptor expression correlated with tumor stroma but not tumor cells.
| S-EPMC5300187 | biostudies-literature
Project description:BackgroundThe impact of oncogene panel results on the surgical management of indeterminate thyroid nodules (ITNs) is currently unknown.MethodsSurgical management of 649 patients consecutively evaluated from October 2008 to April 2016 with a single nodule biopsied and indeterminate cytology (193 evaluated with and 456 without oncogene panels) was assessed and compared. Histological features of 629 consecutively resected ITNs (164 evaluated with and 465 without oncogene panels) were also characterized and compared.ResultsOncogene panel evaluation was associated with higher rates of total thyroidectomy (45% vs 28%; P = .006), and central lymph node dissection (19% vs 12%; P = .03) without increasing the yield of malignancy or decreasing the rate of completion thyroidectomy. Most malignancies (64%), including 83% of those with driver mutation identified, were low-risk cancers for which a lobectomy could have been sufficient initial treatment.ConclusionCurrent oncogene panel results seem insufficient to guide the surgical extent of solitary ITNs.
| S-EPMC7771319 | biostudies-literature
Project description:Background:Up to 30% of thyroid nodules undergoing fine needle aspiration (FNA) yield an indeterminate result. Recent research efforts have suggested that nuclear morphometry and morphology may enhance the diagnostic accuracy of FNA as an objective adjunct. We applied nuclear morphometric analysis on a diverse cohort of patients to evaluate the association between nuclear morphometry and malignancy. Methods:Forty-five randomly selected patients, who underwent thyroid surgery after an indeterminate FNA result (Bethesda III & IV) between 2012-2015, were reviewed. One hundred representative nuclei per FNA of a thyroid nodule were analyzed using ImageJ. Seven validated morphometric parameters were collected: nuclear area, perimeter, circularity, aspect ratio, roundness, and maximum/minimum Feret's diameter. L/S ratio was subsequently calculated. All 8 nuclear parameters were reported as averages with standard errors of the mean (SEM). A Student's t-test was used to assess the association of nuclear parameters with final surgical pathology. Results:The mean age of all patients was 56.31±15.39 years, with female patients comprising 68.9% of the cohort. Twenty-two patients had malignant thyroid nodules. The mean perimeter of nuclei for the cohort was 18.48±0.45 µm, the mean area was 22.19±0.93 µm, and the mean maximum Feret's diameter was 6.67±0.13 µm. No significant differences in the 8 nuclear parameters were observed between the malignant and non-malignant groups. Conclusions:In the population examined, our results suggest that nuclear morphometry is not yet a tool of reliable diagnostic value in accessing malignant and non-malignant thyroid nodules. Further investigation is necessary to identify objective parameters that will enhance diagnostic accuracy of indeterminate FNA cytology to minimize the number of diagnostic thyroid surgery.
| S-EPMC7225500 | biostudies-literature
Project description:BackgroundThe accurate classification of lung nodules is critical to achieving personalized lung cancer treatment and prognosis prediction. The treatment options for lung cancer and the prognosis of patients are closely related to the type of lung nodules, but there are many types of lung nodules, and the distinctions between certain types are subtle, making accurate classification based on traditional medical imaging technology and doctor experience challenging. This study adopts a novel approach, using computed tomography (CT) radiomics to analyze the quantitative features in CT images to reveal the characteristics of lung nodules, and then employs diversity-weighted ensemble learning to enhance the accuracy of classification by integrating the predictive results of multiple models.MethodsWe extracted lung nodules from the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and derived radiomics features from the nodules. For the classification tasks of seven types of lung nodules, each was split into binary classifications. Two model-building methods were employed: M1 (equal-weighted voting ensemble classifier) and M2 (diversity-weighted voting ensemble classifier). Models were evaluated using 10-fold cross-validation with metrics including the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.ResultsBoth methods effectively completed classification tasks. The M2 method outperformed M1, particularly in classifying texture, calcification, and the benign and malignant nature of lung nodules. The AUC values of the M2 method in the four subclassifications of texture types of lung nodules were 0.9913, 0.8838, 0.9525, and 0.8845, with the corresponding accuracies of 0.9651, 0.8116, 0.9000, and 0.8284, respectively. In the classification of the degree of calcification of lung nodules, the AUC value of the M2 method was 0.9775 with an accuracy of 0.9642. In the classification of the benign and malignant nature of lung nodules, the AUC value of the M2 method was 0.8953 with an accuracy of 0.8168. The combination of CT radiomics and diversity-weighted ensemble learning effectively identifies lung nodule types, providing a novel method for the precise classification of lung nodules and aiding personalized lung cancer treatment and prognosis prediction.ConclusionsThe combination of CT radiomics and ensemble learning for diversity weighting can be well realized to identify the type of lung nodules.
| S-EPMC11651971 | biostudies-literature
Project description:ObjectivesTo explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery.MethodsThe retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA).ResultsThe DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model.ConclusionDECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.
| S-EPMC10449576 | biostudies-literature
Project description:BackgroundThere is a risk of over investigation and delayed treatment in the work up of solid nodules. Thus, the aim of our study was to develop and validate an integrated model that estimates the malignant risk for indeterminate pulmonary solid nodules (IPSNs).MethodsPatients included in this study with IPSNs who was diagnosed malignant or benign by histopathology. Univariate and multivariate logistic regression were used to build integrated model based on clinical, circulating tumor cells (CTCs) and radiomics features. The performance of the integrated model was estimated by applying receiver operating characteristic (ROC) analysis, and tested in different nodules size and intermediate risk IPSNs. Net reclassification index (NRI) was applied to quantify the additional benefit derived from the integrated model.ResultsThe integrated model yielded areas under the ROC curves (AUCs) of 0.83 and 0.76 in internal and external set, respectively, outperforming CTCs (0.70, P=0.001; 0.68, P=0.128), the Mayo clinical model (0.68, P<0.001; 0.55, P=0.007), the and radiomics model (0.72, P=0.002; 0.67, P=0.050) in both validation sets. Robust performance with high sensitivity up to 98% was also maintained in IPSNs with different solid size and intermediate risk probability. The performance of the integrated model was comparable with positron emission tomography/computed tomography (PET-CT) examination (P=0.308) among the participants with established PET-CT records. NRI demonstrated that the integrated model provided net reclassification of at least 10% on the external validation set compared with single CTCs test.ConclusionsThe integrated model could complement conventional risk models to improve the diagnosis of IPSNs, which is not inferior to PET-CT and could help to guide clinician's decision-making on clinically specific population.
| S-EPMC10087991 | biostudies-literature