Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening

Objectives: To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). Methods: A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results: The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801–0.935), 0.929 (95% CI, 0.862–0.971), 0.941 (95% CI, 0.876–0.978), and 0.884 (95% CI, 0.805–0.938) in the primary cohort and 0.897 (95% CI, 0.768–0.968), 0.933 (95% CI, 0.815–0.986), 0.901 (95% CI, 0.773–0.970), and 0.828 (95% CI, 0.685–0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). Conclusions: The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be preoperative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. Advances in knowledge: The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be preoperative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.

Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules

To investigate whether the maximum standardized uptake value (SUVmax) of 18F-deoxyglucose (FDG) PET imaging can increase the diagnostic efficiency of CT radiomics-based prediction model in differentiating benign and malignant pulmonary ground-glass nodules (GGNs). We retrospectively collected 190 GGNs from 165 patients who underwent 18F-FDG PET/CT examination from January 2012 to March 2020. Propensity score matching (PSM) was performed to select GGNs with similar baseline characteristics. LIFEx software was used to extract 49 CT radiomic features, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to select parameters and establish the Rad-score. Logistic regression analysis was performed combined with semantic features to construct a CT radiomics model, which was combined with SUVmax to establish the PET + CT radiomics model. Receiver operating characteristic (ROC) was used to compare the diagnostic efficacy of different models. After PSM at 1:4, 190 GGNs were divided into benign group (n = 23) and adenocarcinoma group (n = 92). After texture analysis, the Rad-score with three CT texture features was constructed for each nodule. Compared with the Rad-score and CT radiomics model (AUC: 0.704 (95%CI: 0.562-0.845) and 0.908 (95%CI: 0.842-0.975), respectively), PET + CT radiomics model had the best diagnostic efficiency (AUC: 0.940, 95%CI: 0.889-0.990), and there was significant difference between each two of them (P = 0.001-0.030). SUVmax can effectively improve CT radiomics model performance in the differential diagnosis of benign and malignant GGNs. PET + CT radiomics might become a noninvasive and reliable method for differentiating of GGNs.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

Objectives The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. Methods Artificial ground-glass nodules (6 mm and 10 mm diameters, −660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. Results DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. Conclusions DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. Advances in knowledge DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

Clinical Characters in Pre-invasive and Invasive Adenocarcinoma with Pulmonary Ground-glass Nodules

Purpose: With the increasing prevalence of pulmonary ground-glass nodules (GGNs) among younger population, its clinicopathologic performance, lung cancer-associated genetic mutation, and immune landscape features between pre-invasive adenocarcinoma and invasive adenocarcinoma (IAC) need to be get well known.Methods: We retrospectively reviewed basic clinical information, analyzed radiological characteristics, and then evaluated the status of mutational hotspots and tumor mutational burden by sequencing genome in tissue. Programmed death ligand 1 (PD-L1) expression was detected by immunohistochemistry staining. Results: Nodules vastly increased the probability of IAC when the diameter of GGNs was more than 1.15 mm or the consolidation-to-tumor ratio was at least 8.5%, with the latter predictor having a better diagnostic specificity. Tumors positive for exon 19 deletion and exon 21 L858R in EGFR mutation had a higher prevalence in IAC. However, there was no difference in PD-L1 expression. As expected, tumor mutational burden in IAC was higher, despite a low background mutational burden as a whole. Conclusions: GGNs should be pay high attention when several aggressive behaviors showed in radiology and inner solid components increased gradually, providing more evidence apt to a diagnosis of IAC. We found that GGNs of IAC performed early genomic alternations events during the slow growth carcinogenesis stage of GGNs, including the most common proto-oncogene EGFR activation, which mainly concentrates on IAC. Indolent GGNs at an early stage usually have negative PD-L1 expression.

Preoperative Small Pulmonary Nodule Localisation Using Hookwires or Coils: Strategy Selection in Adverse Events

Objective We conducted a retrospective study of adverse events associated with the preoperative procedure of computed tomography (CT)–guided hookwire or coil localisation. We analysed the experience of and process flaws in resecting ground-glass nodules (GGNs) using video-assisted thoracoscopic surgery (VATS) and determined the remedial strategy. Methods Adverse events were evaluated in 20 patients with 25 GGNs who underwent CT-guided hookwire or coil localisation before VATS. For lesions not successfully marked or detected, palpation, resection of the highly suspected area, segmentectomy or lobectomy was performed. Results Among all adverse events, 10 were dislodgement of the marking materials, 2 were breakaway of the marking materials, 4 were >2-cm distance between the lesions and the tip, one was marking material across the two adjacent lobes, 10 were pneumothorax and two were certain parts of marking materials stuck into the walls. All GGNs were resected successfully. Fifteen lesions were detected by palpation. Three GGNs were discovered after the resection of highly suspected areas. The GGNs were removed by lobectomy. Segmentectomies and lobectomies were performed directly on two and four GGNs, respectively. Conclusions When adverse events occur, a second localisation, intraoperative localisation, resection of a highly suspected area, or a segmentectomy or lobectomy can be successfully attempted using VATS for resection of GGNs.