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Malignancy Prediction Capacity and Possible Prediction Model of Circulating Tumor Cells for Suspicious Pulmonary Lesions.

ABSTRACT: More and more undetermined lung lesions are being identified in routine lung cancer screening. The aim of this study was to try to establish a malignancy prediction model according to the tumor presentations. From January 2017 to December 2018, 50 consecutive patients who were identified with suspicious lung lesions were enrolled into this study. Medical records were reviewed and tumor macroscopic and microscopic presentations were collected for analysis. Circulating tumor cells (CTC) were found to differ between benign and malignant lesions (p = 0.03) and also constituted the highest area under the receiver operation curve other than tumor presentations (p = 0.001). Since tumor size showed the highest sensitivity and CTC revealed the best specificity, a malignancy prediction model was proposed. Akaike information criterion (A.I.C.) of the combined malignancy prediction model was 26.73, which was lower than for tumor size or CTCs alone. Logistic regression revealed that the combined malignancy prediction model showed marginal statistical trends (p = 0.0518). In addition, the 95% confidence interval of combined malignancy prediction model showed less wide range than tumor size ≥ 0.7 cm alone. The calculated probability of malignancy in patients with tumor size ≥ 0.7 cm and CTC > 3 was 97.9%. By contrast, the probability of malignancy in patients whose tumor size was < 0.7 cm, and CTC ≤ 3 was 22.5%. A combined malignancy prediction model involving tumor size followed by the CTC count may provide additional information to assist decision making. For patients who present with tumor size ≥ 0.7 cm and CTC counts > 3, aggressive management should be considered, since the calculated probability of malignancy was 97.9%.

SUBMITTER: Wu CY

PROVIDER: S-EPMC8223995 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeLow-dose computed tomography (LDCT) screening of high-risk patients decreases lung cancer-related mortality. However, high false-positive rates associated with LDCT result in unnecessary interventions. To distinguish non-small-cell lung cancer (NSCLC) from benign nodules, in the present study, we integrated cellular liquid biomarkers in patients with suspicious lung nodules (lung cancer screening reporting and data system [Lung-RADS] 4).MethodsProspectively, 7.5 mL of blood was collected from 221 individuals (training set: 90 nonscreened NSCLC patients, 74 high-risk screening patients with no/benign nodules [Lung-RADS 1-3], and 20 never smokers; validation set: 37 patients with suspicious nodules [Lung-RADS 4]). Circulating tumor cells (CTCs), CTC clusters, and tumor-macrophage fusion (TMF) cells were identified by blinded analyses. Screening patients underwent a median of two LDCTs (range, 1-4) with a median surveillance time of 30 (range, 11-50) months.ResultsIn the validation set of 37 Lung-RADS 4 patients, all circulating cellular biomarker counts (P < .005; Wilcoxon test) and positivity rates were significantly higher in 23 biopsy-proven NSCLC patients (CTCs: 23 of 23 [100%], CTC clusters: 6 of 23 [26.1%], and TMF cells: 15 of 23 [65.2%]) than in 14 patients with biopsy-proven benign nodules (6 of 14 [42.9%], 0 of 14 [0%], and 2 of 14 [14.3%]). On the basis of cutoff values from the training set, logistic regression with receiver operating characteristic and area under the curve analyses demonstrated that CTCs (sensitivity: 0.870, specificity: 1.0, and area under the curve: 0.989) and TMF cells (0.652; 0.880; 0.790) complement LDCT in diagnosing NSCLC in Lung-RADS 4 patients.ConclusionCellular liquid biomarkers have a potential to complement LDCT interpretation of suspicious Lung-RADS 4 nodules to distinguish NSCLC from benign lung nodules. A future prospective, large-scale, multicenter clinical trial should validate the role of cellular liquid biomarkers in improving diagnostic accuracy in high-risk patients with Lung-RADS 4 nodules.

Project description:PurposePatient-tailored management of thyroid nodules requires improved risk of malignancy stratification by accurate preoperative nodule assessment, aiming to personalize decisions concerning diagnostics and treatment. Here, we perform an exploratory pilot study to identify possible patterns on multispectral optoacoustic tomography (MSOT) for thyroid malignancy stratification. For the first time, we directly correlate MSOT images with histopathology data on a detailed level.MethodsWe use recently enhanced data processing and image reconstruction methods for MSOT to provide next-level image quality by means of improved spatial resolution and spectral contrast. We examine optoacoustic features in thyroid nodules associated with vascular patterns and correlate these directly with reference histopathology.ResultsOur methods show the ability to resolve blood vessels with diameters of 250 μm at depths of up to 2 cm. The vessel diameters derived on MSOT showed an excellent correlation (R2-score of 0.9426) with the vessel diameters on histopathology. Subsequently, we identify features of malignancy observable in MSOT, such as intranodular microvascularity and extrathyroidal extension verified by histopathology. Despite these promising features in selected patients, we could not determine statistically relevant differences between benign and malignant thyroid nodules based on mean oxygen saturation in thyroid nodules. Thus, we illustrate general imaging artifacts of the whole field of optoacoustic imaging that reduce image fidelity and distort spectral contrast, which impedes quantification of chromophore presence based on mean concentrations.ConclusionWe recommend examining optoacoustic features in addition to chromophore quantification to rank malignancy risk. We present optoacoustic images of thyroid nodules with the highest spatial resolution and spectral contrast to date, directly correlated to histopathology, pushing the clinical translation of MSOT.

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Malignancy Prediction Capacity and Possible Prediction Model of Circulating Tumor Cells for Suspicious Pulmonary Lesions.

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