Purpose: No study has investigated the precise perioperative dynamic changes in circulating tumor DNA (ctDNA) in any patients with early-stage cancer. This study (DYNAMIC) investigated perioperative dynamic changes in ctDNA and determined the appropriate detection time of ctDNA-based surveillance for surgical patients with lung cancer.Experimental Design: Consecutive patients who underwent curative-intent lung resections were enrolled prospectively (NCT02965391). Plasma samples were obtained at multiple prespecified time points including before surgery (time A), during surgery after tumor resection (time B-time D), and after surgery (time P1-time P3). Next-generation sequencing-based detection platform was performed to calculate the plasma mutation allele frequency. The primary endpoint was ctDNA half-life after radical tumor resection.Results: Thirty-six patients showed detectable mutations in time A. The plasma ctDNA concentration showed a rapid decreasing trend after radical tumor resection, with the average mutant allele fraction at times A, B, C, and D being 2.72%, 2.11%, 1.14%, and 0.17%, respectively. The median ctDNA half-life was 35.0 minutes. Patients with minimal residual disease (MRD) detection had a significant slower ctDNA half-life than those with negative MRD (103.2 minutes vs. 29.7 minutes, P ¼ 0.001). The recurrence-free survival of patients with detectable and undetectable ctDNA concentrations at time P1 was 528 days and 543 days, respectively (P ¼ 0.657), whereas at time P2 was 278 days and 637 days, respectively (P ¼ 0.002).Conclusions: ctDNA decays rapidly after radical tumor resection. The ctDNA detection on the third day after R0 resection can be used as the baseline value for postoperative lung cancer surveillance.

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Purpose: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making. Experimental Design: A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort. Results: The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854–0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859–0.916), higher than those of the Brock model [0.806 (0.771–0.838)], PKU model [0.780 (0.743–0.817)], Mayo model [0.739 (0.697–0.776)], and VA model [0.682 (0.640–0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815–0.915)] was higher than that of surgeons [0.790 (0.711–0.852), 0.741 (0.662–0.804), and 0.727 (0.650–0.788)], radiologist [0.748 (0.671–0.814)], and the CADx system [0.757 (0.682–0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity. Conclusions: After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.

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ObjectiveThis study aims to compare the clinical and pathological characteristics between patients undergoing surgery for extremely multiple ground-glass nodules (GGNs) and those for single GGN.MethodsWe defined extremely multiple GGNs as follows: (i) number of GGNs ≥3, (ii) GGN diameter between 3 and 30 mm, and (iii) no less than three nodules that were surgically removed and pathologically diagnosed. Patients with extremely multiple GGNs and single GGNs who underwent surgery at the same time were retrospectively analyzed. The patients were divided into three groups according to the number of nodules: exceedingly multiple nodules (EMN) group (>10), highly multiple nodules (HMN) group (three to 10), and single nodule (SN) group. The clinical and pathological characteristics, surgical methods and prognosis were analyzed.ResultsNinety-nine patients with single nodules and 102 patients with extremely multiple nodules were enrolled. Among the patients with extremely multiple nodules, 43 (42.2%) had >10 nodules. There were no significant differences in demographic characteristics, such as age, sex, and smoking history, between the groups, but there were differences in tumor characteristics. All patients with >10 nodules showed bilateral pulmonary nodules and presented with both pure and mixed GGNs. The single GGNs were smaller in diameter, and the proportion of mixed GGNs and pathologically invasive adenocarcinoma was lower than that of the primary nodules in the exceedingly multiple GGNs group (p < 0.05). However, the proportion of both mixed GGNs and malignant nodules decreased significantly with the increasing number of total lesions. During postoperative follow-up, one patient in the highly multiple nodules group had a local recurrence, and 16 (15.7%) patients in the extremely multiple GGNs group and 10 (9.8%) patients in the single GGN group had enlarged unresected GGNs or additional GGNs.ConclusionsOur study revealed the clinical and pathologic characteristics, surgical methods, and prognosis of patients with extremely multiple GGNs and compared them with those of patients with a single GGN. Although the primary nodules in extremely multiple GGNs may have higher malignancy than those in the single nodule group, the proportion of both mGGNs and malignant nodules decreased significantly with the increasing number of lesions, and the prognosis of patients with extremely multiple GGNs was satisfied.