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Background: The most effective method for improving the prognosis of lung cancer is the application of low-dose computed tomography (LDCT) for pulmonary nodule screening in populations at high risk. Timely diagnosis and treatment of early-stage lung cancer can contribute to higher long-term survival rates. However, it remains difficult to differentiate malignant from benign pulmonary nodules measuring 8–15 mm, and avoid overtreatment on the one hand and delayed diagnosis on the other hand. In this consensus paper, we aimed to clarify the definition of “high-risk indeterminate pulmonary nodules (IPNs)” and discuss appropriate evaluation and management to facilitate timely diagnosis of lung cancer to improve lung cancer outcome. Direction for future research was discussed. Methods: A multi-disciplinary panel of doctors and IT experts from Asia, Canada and the U.S. were invited to participate. Published evidence and consensus guidelines were used to develop this consensus was clarified. Their evaluation and management were discussed. Findings: The experts believed that the prevalence of pulmonary nodules was very high, and it that was difficult to diagnose early-stage lung cancer due to the small size of the nodules, often leading to delayed diagnosis or overtreatment. To address this issue and to improve long-term outcome, the panel considered important to revise the classification of high-risk IPNs, (1) as pulmonary nodules that cannot be clearly diagnosed with non-surgical biopsy procedures, but is highly suspicious for early-stage lung cancer. The panel also recommends the most responsible should arrange imaging evaluations and follow-ups, taking new technologies into account. Artificial intelligence (AI) assessment based on the Medical Internet of Things (MIoT) can be combined with expert opinion to form a human–computer multidisciplinary team (MDT) that can fully implement the three core procedures of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing. This will help to upgrade the non-standard diagnosis and treatment, the so-called “handicraft workshop model”, to a modern assembly-line model that meets international standards. The MIoT technology, which has the potential to realize “simplification of complex problems, digitalization of simple problems, programming of digital problems, and systematization of programming problems”, can promote the homogeneous evaluation of pulmonary nodules by enhancing both the sensitivity and the specificity of detecting early-stage lung cancer, in order to avoid delayed diagnosis and overtreatment. Conclusion: To optimize the evaluation of early-stage lung cancer, and to avoid delayed diagnosis and overtreatment, it is necessary to propose and promote the concept of “high-risk IPNs”. The application of current technologies, AI, and a human–computer MDT, will facilitate improvement in nodule evaluation, transforming the current diagnosis and treatment model, which is akin to production in handicraft workshops, into a modern assembly-line model that meets international standards, and will ultimately result in better prognosis.

Activation of STING signaling in DCs promotes antitumor immunity. Aerobic glycolysis is a metabolic hallmark of activated DCs, but how the glycolytic pathway intersects with STING signaling in tumor-infiltrating DCs remains elusive. Here, we show that glycolysis drives STING signaling to facilitate DC-mediated antitumor immune responses. Tumor-infiltrating DCs exhibited elevated glycolysis, and blockade of glycolysis by DC-specific Ldha/Ldhb double deletion resulted in defective antitumor immunity. Mechanistically, glycolysis augmented ATP production to boost STING activation and STING-dependent DC antitumor functions. Moreover, DC-intrinsic STING activation accelerated HIF-1[alpha]-mediated glycolysis and established a positive feedback loop. Importantly, glycolysis facilitated STING-dependent DC activity in tissue samples from patients with non-small cell lung cancer. Our results provide mechanistic insight into how the crosstalk of glycolytic metabolism and STING signaling enhances DC antitumor activity and can be harnessed to improve cancer therapies., Introduction Tumor-infiltrating DCs present tumor antigens and provide cytokine signals to initiate productive antitumor T cell immunity (1, 2). Tumor DNA (Tu-DNA) uptake by intratumoral DCs triggers the activation of [...]

Robotic‐assisted bronchoscopy (RAB) is a newly developed bronchoscopic technique for the diagnosis of peripheral pulmonary lesions (PPLs). The objective of this meta‐analysis was to analyze the diagnostic yield and safety of RAB in patients with PPLs. Five databases (PubMed, Embase, Web of Science, CENTRAL, and ClinicalTrials.gov) were searched from inception to April 2023. Two independent investigators screened retrieved articles, extracted data, and assessed the study quality. The pooled diagnostic yield and complication rate were estimated. Subgroup analysis was used to explore potential sources of heterogeneity. Publication bias was assessed using funnel plots and the Egger test. Sensitivity analysis was also conducted to assess the robustness of the synthesized results. A total of 725 lesions from 10 studies were included in this meta‐analysis. No publication bias was found. Overall, RAB had a pooled diagnostic yield of 80.4% (95% CI: 75.7%–85.1%). Lesion size of >30 mm, presence of a bronchus sign, and a concentric radial endobronchial ultrasound view were associated with a statistically significantly higher diagnostic yield. Heterogeneity exploration showed that studies using cryoprobes reported better yields than those without cryoprobes (90.0%, 95% CI: 83.2%–94.7% vs. 79.0%, 95% CI: 75.8%–82.2%, p < 0.01). The pooled complication rate was 3.0% (95% CI: 1.6%–4.4%). In conclusion, RAB is an effective and safe technique for PPLs diagnosis. Further high‐quality prospective studies still need to be conducted. [ABSTRACT FROM AUTHOR]

Introduction: Cryobiopsy (CB) using a 1.1-mm cryoprobe under fluoroscopic guidance is feasible and safe for diagnosis of ground glass opacity (GGO) lesions. However, the efficacy of CB combined with cone-beam CT (CBCT) for GGO-predominant pulmonary nodules remains elusive. Methods: We retrospectively studied patients who underwent CB combined with conventional biopsy under CBCT guidance for GGO-predominant pulmonary nodules with a consolidation-to-tumour ratio <50.0%. Results: A total of 32 patients with GGO-predominant pulmonary nodules were enrolled: 17 pure GGOs and 15 mixed GGOs. The mean lesion diameter was 15.81 ± 5.52 mm and the overall diagnostic yield was 71.9%. Seven lesions were diagnosed by CB alone, which increased the diagnostic outcomes by 21.9%. Diagnostic yields for CB, forceps biopsy (FB), brushing, and guide sheath flushing were 65.6%, 46.9%, 15.6%, and 14.3%, respectively. Univariate analysis revealed that positive computed tomography (CT) bronchus sign (p = 0.035), positive CBCT sign (p < 0.01), and CB-first biopsy sequence (p = 0.036) were significant predictive factors for higher diagnostic yield. Specimens obtained by CB had larger mean sample size (p < 0.01), lower blood cell area (p < 0.01), and fewer crush artefacts (p < 0.01) than specimens from FB. No severe bleeding or other complications occurred. Conclusion: CB using a 1.1-mm cryoprobe under CBCT guidance increased diagnostic yield for GGO-predominant pulmonary nodules based on conventional biopsy. Further, it provided larger and nearly intact samples compared with forceps. [ABSTRACT FROM AUTHOR]