Impact of lymphadenectomy extent on immunotherapy efficacy in postresectional recurred non-small cell lung cancer: a multi-institutional retrospective cohort study

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study

OBJECTIVES

Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.

METHODS

This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps.

RESULTS

The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively.

CONCLUSIONS

By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

Lung adenocarcinoma manifested as ground-glass nodules in teenagers: characteristics, surgical outcomes and management strategies

OBJECTIVES

Ground-glass nodules-featured lung cancer have been identified in some teenagers in recent years. This study aims to investigate the characteristics and surgical outcomes of these patients and explore proper management strategy.

METHODS

Patients aged ≤20 with incidentally diagnosed lung cancer were retrospectively reviewed from February 2016 to March 2023. Based on lymph node evaluation status, these patients were divided into non-lymph node evaluation and lymph node evaluation groups. The clinical and pathological characteristics were analysed.

RESULTS

A total of 139 teenage patients were included, with an obviously increased cases observed from 2019, corresponding to the COVID-19 pandemic. The median age of the 139 patients was 18 years (range 12-20). Eighty-five patients had pure ground-glass nodules, while others had mixed ground-glass nodules. The mean diameter of nodules was 8.87 ± 2.20 mm. Most of the patients underwent wedge resection (64%) or segmentectomy (31.7%). Fifty-two patients underwent lymph node sampling or dissection. None of these patients had lymph node metastasis. The majority of lesions were adenocarcinoma in situ (63 cases) and minimally invasive adenocarcinoma (72 cases), while four lesions were invasive adenocarcinoma. The median follow-up time was 2.46 years, and none of these patients experienced recurrence or death during follow-up. The lymph node evaluation group had longer hospital stays (P < 0.001), longer surgery time (P < 0.001), and greater blood loss (P = 0.047) than the non-lymph node evaluation group.

CONCLUSIONS

The COVID-19 pandemic significantly increased the number of teenage patients incidentally diagnosed with lung cancer, presenting as ground-glass nodules on CT scans. These patients have favourable surgical outcomes. We propose a management strategy for teenage patients, and suggest that sub-lobar resection without lymph node dissection may be an acceptable surgical procedure for these patients.

Prognostic patterns in invasion lymph nodes of lung adenocarcinoma reveal distinct tumor microenvironments

Tumor-draining lymph nodes (TDLNs) are usually the first station of tumor metastasis in lung cancer. TDLNs+ have distinct pathomorphologic and tumor microenvironment (TME)-compositional patterns, which still need to be thoroughly investigated in lung adenocarcinoma (LUAD). Here, we enrolled 312 LUAD patients with TDLNs+ from our institution between 2015 and 2019. 3DHISTECH was used to scan all of the TDLNs+. Based on morphologic features, TDLNs+ patterns were classified as polarized-type or scattered-type, and TME-compositional patterns were classified as colloid-type, necrosis-type, specific-type, and common-type. Multivariate analysis revealed an increased risk of early recurrence associated with scattered-type (HR 2.37, 95% CI: 1.06-5.28), colloid-type (HR 1.95, 95% CI: 1.03-3.67), and necrosis-type (HR 2.21, 95% CI: 1.13-4.89). NanoString transcriptional analysis revealed an immunosuppression and vascular invasion hallmark in scattered and necrosis patterns and an immunoactivated hallmark in polarized and common patterns. According to imaging mass cytometry (IMC), the scattered and necrosis patterns revealed that germinal centers (GC) were compromised, GCB cell and T cell proliferation were deficient, tumor cells had the potential for proliferation, and the immune attack may be weaker. In this study, we present evidence that LUAD patients have distinct patterns and immune hallmarks of TDLNs+ related to their prognosis.

Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level

Background

In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%.

Methods

In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis.

Results

The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases.

Conclusions

Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.

Nanotheranostics in cancer lymph node metastasis: The long road ahead

Lymph node metastasis (LNM) significantly impacts the prognosis of cancer patients. Despite significant advancements in diagnostic techniques and treatment modalities, clinical challenges continue to persist in the realm of LNM. These include difficulties in early diagnosis, limited treatment efficacy, and potential side effects and injuries associated with treatment. Nanotheranostics, a field within nanotechnology, seamlessly integrates diagnostic and therapeutic functionalities. Its primary goal is to provide precise and effective disease diagnosis and treatment simultaneously. The development of nanotheranostics for LNM offers a promising solution for the stratified management of patients with LNM and promotes the advancement of personalized medicine. This review introduces the mechanisms of LNM and challenges in its diagnosis and treatment. Furthermore, it demonstrates the advantages and development potential of nanotheranostics, focuses on the challenges nanotheranostics face in its application, and provides an outlook on future trends. We consider nanotheranostics a promising strategy to improve clinical effectiveness and efficiency as well as the prognosis of cancer patients with LNM.