Neoadjuvant sintilimab plus chemotherapy in EGFR-mutant NSCLC: Phase 2 trial interim results (NEOTIDE/CTONG2104).

The clinical efficacy of neoadjuvant immunotherapy plus chemotherapy remains elusive in localized epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC). Here, we report interim results of a Simon's two-stage design, phase 2 trial using neoadjuvant sintilimab with carboplatin and nab-paclitaxel in resectable EGFR-mutant NSCLC. All 18 patients undergo radical surgery, with one patient experiencing surgery delay. Fourteen patients exhibit confirmed radiological response, with 44% achieving major pathological response (MPR) and no pathological complete response (pCR). Similar genomic alterations are observed before and after treatment without influencing the efficacy of subsequent EGFR-tyrosine kinase inhibitors (TKIs) in vitro. Infiltration and T cell receptor (TCR) clonal expansion of CCR8+ regulatory T (Treg)hi/CXCL13+ exhausted T (Tex)lo cells define a subtype of EGFR-mutant NSCLC highly resistant to immunotherapy, with the phenotype potentially serving as a promising signature to predict immunotherapy efficacy. Informed circulating tumor DNA (ctDNA) detection in EGFR-mutant NSCLC could help identify patients nonresponsive to neoadjuvant immunochemotherapy. These findings provide supportive data for the utilization of neoadjuvant immunochemotherapy and insight into immune resistance in EGFR-mutant NSCLC.

Neoadjuvant nivolumab with or without platinum-doublet chemotherapy based on PD-L1 expression in resectable NSCLC (CTONG1804): a multicenter open-label phase II study.

This prospective multicenter phase II study evaluated the clinical efficacy of neoadjuvant nivolumab-exclusive (N) and nivolumab-chemotherapy (N/C) combinations based on PD-L1 expression. Eligible patients exhibited resectable clinical stage IIA-IIIB (AJCC 8th edition) NSCLC without EGFR/ALK alterations. Patients received either mono-nivolumab (N) or nivolumab + nab-paclitaxel+ carboplatin (N/C) for three cycles based on PD-L1 expression. The primary endpoint was the major pathological response (MPR). Key secondary endpoints included the pathologic complete response (pCR), objective response rate (ORR), and event-free survival (EFS). Baseline PD-L1 expression and perioperative circulating tumor DNA (ctDNA) status were correlated with pCR and EFS. Fifty-two patients were enrolled, with 46 undergoing surgeries. The MPR was 50.0% (26/52), with 25.0% (13/52) achieving pCR, and 16.7% and 66.7% for patients with PD-L1 ≥ 50% in N and N/C groups, respectively. Thirteen (25.0%) patients experienced grade 3 or higher immune-related adverse events during neoadjuvant treatment. Patients with post-neoadjuvant ctDNA negativity was more likely to have pCR (39.1%) compared with those remained positive (6.7%, odds ratio = 6.14, 95% CI 0.84-Inf, p = 0.077). With a median follow-up of 25.1 months, the 18-month EFS rate was 64.8% (95% CI 51.9-81.0%). For patients with ctDNA- vs. ctDNA + , the 18m-EFS rate was 93.8% vs 47.3% (HR, 0.15; 95% CI 0.04, 0.94; p = 0.005). Immunochemotherapy may serve as an optimal neoadjuvant treatment even for patients with PD-L1 expression ≥ 50%. ctDNA negativity following neoadjuvant treatment and surgery could help identify superior pathological and survival benefits, which requires further confirmation in a prospective clinical trial (NCT04015778).

Spatial downregulation of CD74 signatures may drive invasive component development in part-solid lung adenocarcinoma.

Pulmonary nodules with part-solid imaging features manifest during the progression from preinvasive to invasive lung adenocarcinoma. To define the spatial composition and evolutionary trajectories of early-stage lung adenocarcinoma, we combined spatial transcriptomics (ST) and pathological annotations from 20 part-solid nodules (PSNs), four of which were matched with single-cell RNA sequencing. Two malignant cell populations (MC1 and MC2) were identified, and a linear evolutionary relationship was observed. Compared to MC2, the pre-existing malignant MC1 exhibited a lower metastatic signature, corresponding to the preinvasive component (lepidic) on pathology and the ground glass component on PSN imaging. Higher immune infiltration was observed among MC1 regions in ST profiles, and further analysis revealed that macrophages may be involved in this process through the CD74 axis. This work provides deeper insights into the evolutionary process and spatial immune cell composition behind PSNs and highlights the mechanisms of immune escape behind this adenocarcinoma trajectory.

Artificial intelligence-quantified tumour-lymphocyte spatial interaction predicts disease-free survival in resected lung adenocarcinoma: A graph-based, multicentre study.

BACKGROUND AND OBJECTIVE: A high degree of lymphocyte infiltration is related to superior outcomes amongst patients with lung adenocarcinoma. Recent evidence indicates that the spatial interactions between tumours and lymphocytes also influence the anti-tumour immune responses, but the spatial analysis at the cellular level remains insufficient. METHODS: We proposed an artificial intelligence-quantified Tumour-Lymphocyte Spatial Interaction score (TLSI-score) by calculating the ratio between the number of spatial adjacent tumour-lymphocyte and the number of tumour cells based on topology cell graph constructed using H&E-stained whole-slide images. The association of TLSI-score with disease-free survival (DFS) was explored in 529 patients with lung adenocarcinoma across three independent cohorts (D1, 275; V1, 139; V2, 115). RESULTS: After adjusting for pTNM stage and other clinicopathologic risk factors, a higher TLSI-score was independently associated with longer DFS than a low TLSI-score in the three cohorts [D1, adjusted hazard ratio (HR), 0.674; 95% confidence interval (CI) 0.463-0.983; p = 0.040; V1, adjusted HR, 0.408; 95% CI 0.223-0.746; p = 0.004; V2, adjusted HR, 0.294; 95% CI 0.130-0.666; p = 0.003]. By integrating the TLSI-score with clinicopathologic risk factors, the integrated model (full model) improves the prediction of DFS in three independent cohorts (C-index, D1, 0.716 vs. 0.701; V1, 0.666 vs. 0.645; V2, 0.708 vs. 0.662) CONCLUSIONS: TLSI-score shows the second highest relative contribution to the prognostic prediction model, next to the pTNM stage. TLSI-score can assist in the characterising of tumour microenvironment and is expected to promote individualized treatment and follow-up decision-making in clinical practice.

Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.

BACKGROUND: Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed. METHODS: In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways. RESULTS: A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72-16.44; P = 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10-6.29, P = 0.0292; V2: HR 2.99, 95%CI 1.34-6.66, P = 0.0075; V3: HR 1.93, 95%CI 1.15-3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent. CONCLUSIONS: MPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care.

Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma.

A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.

Venous malformation presenting as Mauritia arabica-like bronchial wall thickness: a case report.

Primary pulmonary venous malformation is rare and usually presents as single or multiple round masses or nodules. Here, we present the first report of a case of venous malformation presenting as Mauritia arabica-like bronchial wall thickness that was initially misdiagnosed as bronchiectasis. A Chinese man in his late 20s presented complaining of hemoptysis for 10 days. Computed tomography demonstrated bronchiectasis and M. arabica-like bronchial wall thickening in the left lower lobe. He was unresponsive to medical treatment for bronchiectasis and underwent thoracoscopic left lower lobectomy. Histopathological examination revealed venous malformation around the bronchial walls with no bronchiectasis. Venous malformation should be considered in the differential diagnosis of bronchiectasis, especially in patients with the following triad of signs: no response to antibiotics, M. arabica-like bronchial wall thickness, and normal accompanying arteries.

Induction immune-checkpoint inhibitors for resectable oncogene-mutant NSCLC: A multicenter pooled analysis.

Despite limited efficacy of immunotherapy for advanced non-small-cell lung cancer (NSCLC) with driver mutations, whether neoadjuvant immunotherapy could be clinically valuable in those patients warrants further investigation. We utilized 40 oncogene-mutant NSCLC treated with induction immunotherapy from a large consecutive multicenter cohort. Overall response rate was 62.5% while 2 patients had disease progression. Of 39 patients that received surgery, R0 resection rate was 97.4%. The major pathological response (MPR) rate was 37.5% and the pathological complete response (pCR) rate was 12.5%. Pre-treatment PD-L1 expression was not a predictive biomarker in these patients. Median disease-free survival for all oncogenic mutation and EGFR mutation was 28.5 months. Indirect comparison through integrating CTONG1103 cohort showed neoadjuvant immunotherapy plus chemotherapy yielded the most superior efficacy among erlotinib and chemotherapy for resectable EGFR-mutant NSCLC. No MPR patients were identified with neoadjuvant immunotherapy plus chemotherapy for uncommon EGFR insertion or point mutations. Our results indicated the potential clinical feasibility of neoadjuvant immunotherapy for resectable localized oncogene-mutant NSCLC especially for EGFR-mutant NSCLC.

Dynamic 18 F-FDG PET/CT can predict the major pathological response to neoadjuvant immunotherapy in non-small cell lung cancer.

Major pathological response (MPR) is a potential surrogate for overall survival. We determined whether the dynamic changes in 18 F-labeled fluoro-2-deoxyglucose positron emission tomography/computed tomography (18 F-FDG PET/CT) were associated with MPR in patients receiving neoadjuvant immunotherapy. Forty-four patients with stage II-III non-small cell lung cancer (NSCLC) who received neoadjuvant immunotherapy and radical surgery were enrolled. Moreover, 18 F-FDG PET/CT scans were performed at baseline and within 1 week before surgery to evaluate the disease. All histological sections were reviewed to assess MPR. The detailed clinical features of the patients were analyzed. The reliability of the clinical variables was assessed in differentiating between MPR and non-MPR using logistic regression. Receiver-operating characteristic (ROC) curve analysis identified the SUVmax changes threshold most associated with MPR. Most of the patients were pathologically diagnosed with squamous cell carcinoma and received anti-PD-1 antibodies plus chemotherapy. The immunotherapy regimens included nivolumab, pembrolizumab, and camrelizumab. MPR was observed in more than half of lesions. Tumors with MPR had a higher decrease in the longest dimension on dynamic PET/CT than those without MPR. Furthermore, the decline in SUVmax was significantly different between MPR and non-MPR diseases, and MPR lesions had a prominent mean reduction in SUVmax. SUVmax reduction was independently associated with MPR in the multivariate regression. On ROC analysis, the threshold of SUVmax decrease in 60% was associated with MPR. Dynamic changes in SUVmax were associated with MPR. The tumors with MPR showed a greater PET/CT response than those without MPR. A SUVmax decrease of more than 60% is more likely to result in an MPR after receiving neoadjuvant immunotherapy.

Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention.

Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heterogeneity, large intraclass variability and small interclass variability make the classification task challenging. In this paper, we propose a novel bilinear convolutional neural network- (Bilinear-CNN-) based model with a bilinear convolutional module and a soft attention module to tackle this problem. This method investigates the intraclass semantic correspondence and focuses on the more distinguishable features that make feature output variations relatively large between interclass. The performance of the Bilinear-CNN-based model is compared with other state-of-the-art methods on the histopathological classification dataset, which consists of 107.7 k patches of lung cancer. We further evaluate our proposed algorithm on an additional dataset from colorectal cancer. Extensive experiments show that the performance of our proposed method is superior to that of previous state-of-the-art ones and the interpretability of our proposed method is demonstrated by Grad-CAM.

Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification.

BACKGROUND: High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. METHODS: We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3+ and CD8+ T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system "I-score" based on the automated assessed cell density. RESULTS: A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33-0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36-0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685). CONCLUSION: This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC.

Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels.

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We propose a two-step model including a classification and a segmentation phases. In the classification phase, we propose a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieve tissue semantic segmentation by our propose Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduce a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conduct several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms five state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling on a randomly sampled 100 patches dataset, patch-level labeling can greatly reduce the annotation time from hours to minutes. The source code and the released datasets are available at: https://github.com/ChuHan89/WSSS-Tissue.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer.

OBJECTIVES: To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction. METHODS: In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55). RESULTS: iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001). CONCLUSIONS: iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection. KEY POINTS: • Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer.

Background: Despite the well-known role of immunoscore, as a prognostic tool, that appeared to be superior to tumor-node-metastasis (TNM) staging system, no prognostic scoring system based on immunohistochemistry (IHC) staining digital image analysis has been established in non-small cell lung cancer (NSCLC). Hence, we aimed to develop and validate an immune-based prognostic risk score (IMPRS) that could markedly improve individualized prediction of postsurgical survival in patients with resected NSCLC. Methods: In this retrospective study, complete resection of NSCLC (stage I-IIIA) was performed for two independent patient cohorts (discovery cohort, n=168; validation cohort, n=115). Initially, paraffin-embedded resected specimens were stained by immunohistochemistry (IHC) of three immune cell types (CD3+, CD4+, and CD8+ T cells), and a total of 5,580 IHC-immune features were extracted from IHC digital images for each patient by using fully automated pipeline. Then, an IHC-immune signature was constructed with selected features using the LASSO Cox analysis, and the association of signature with patients' overall survival (OS) was analyzed by Kaplan-Meier method. Finally, IMPRS was established by incorporating IHC-immune signature and independent clinicopathological variables in multivariable Cox regression analysis. Furthermore, an external validation cohort was included to validate this prognostic risk score. Results: Eight key IHC-immune features were selected for the construction of IHC-immune signature, which showed significant associations with OS in all cohorts [discovery: hazard ratio (HR)=11.518, 95%CI, 5.444-24.368; validation: HR=2.664, 95%CI, 1.029-6.896]. Multivariate analyses revealed IHC-immune signature as an independent prognostic factor, and age, T stage, and N stage were also identified and entered into IMPRS (all p<0.001). IMPRS had good discrimination ability for predicting OS (C-index, 0.869; 95%CI, 0.861-0.877), confirmed using external validation cohort (0.731, 0.717-0.745). Interestingly, IMPRS had better prognostic value than clinicopathological-based model and TNM staging system termed as C-index (clinicopathological-based model: 0.674; TNM staging: 0.646, all p<0.05). More importantly, decision curve analysis showed that IMPRS had adequate performance for predicting OS in resected NSCLC patients. Conclusions: Our findings indicate that the IMPRS that we constructed can provide more accurate prognosis for individual prediction of OS for patients with resected NSCLC, which can help in guiding personalized therapy and improving outcomes for patients.

A radiomics prognostic scoring system for predicting progression-free survival in patients with stage IV non-small cell lung cancer treated with platinum-based chemotherapy.

OBJECTIVE: To develop and validate a radiomics prognostic scoring system (RPSS) for prediction of progression-free survival (PFS) in patients with stage IV non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy. METHODS: In this retrospective study, four independent cohorts of stage IV NSCLC patients treated with platinum-based chemotherapy were included for model construction and validation (Discovery: n=159; Internal validation: n=156; External validation: n=81, Mutation validation: n=64). First, a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography (CT) images of each patient. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator method (LASSO) penalized Cox regression analysis. Finally, an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction. RESULTS: The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts (All P<0.05). On the multivariable analysis, independent factors for PFS were radiomics signature, performance status (PS), and N stage, which were all selected into construction of RPSS. The RPSS showed significant prognostic performance for predicting PFS in discovery [C-index: 0.772, 95% confidence interval (95% CI): 0.765-0.779], internal validation (C-index: 0.738, 95% CI: 0.730-0.746), external validation (C-index: 0.750, 95% CI: 0.734-0.765), and mutation validation (C-index: 0.739, 95% CI: 0.720-0.758). Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness (All P<0.05). CONCLUSIONS: This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stage IV NSCLC patients treated with platinum-based chemotherapy, which holds promise for guiding personalized pre-therapy of stage IV NSCLC.

Multiomics analysis reveals a distinct response mechanism in multiple primary lung adenocarcinoma after neoadjuvant immunotherapy.

Multiple primary lung cancer (MPLC) remains a tough challenge to diagnose and treat. Although neoadjuvant immunotherapy has shown promising results in early stage non-small cell lung cancer, whether such modality can benefit all primary lesions remains unclear. Herein, we performed integrated multiomics analysis in one patient with early stage MPLC with remarkable tumor shrinkage in a solid nodule and no response in two subsolid nodules after treatment with three cycles of neoadjuvant pembrolizumab. Genomic heterogeneity was observed among responding nodules with high levels of infiltrating CD8+ and CD68+ immune cells. Substantially downregulated human leukocyte antigen (HLA)-related genes and impaired T lymphocyte function were observed in non-responding nodules. A larger proportion of infiltrating tissue resident memory T cells (Trm) along with high T cell receptor repertoire clonality in responding nodules were validated as predictive and prognostic biomarkers in multiple cancer types using external public datasets. These results suggested that neoadjuvant programmed death 1 (PD-1)/programmed death ligand 1 inhibitors alone may not be an optimal therapeutic strategy for MPLC due to disparities in genomic alterations and immune microenvironment among different lesions. Additionally, we postulate that increased infiltration of Trm may be a unique marker of early immune responses to PD-1 blockade.

Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation.

Nuclei segmentation is a vital step for pathological cancer research. It is still an open problem due to some difficulties, such as color inconsistency introduced by non-uniform manual operations, blurry tumor nucleus boundaries and overlapping tumor cells. In this paper, we aim to leverage the unique optical characteristic of H&E staining images that hematoxylin always stains cell nuclei blue, and eosin always stains the extracellular matrix and cytoplasm pink. Therefore, we extract the Hematoxylin component from RGB images by Beer-Lambert's Law. According to the optical attribute, the extracted Hematoxylin component is robust to color inconsistency. With the Hematoxylin component, we propose a Hematoxylin-aware CNN model for nuclei segmentation without the necessity of color normalization. Our proposed network is formulated as a Triple U-net structure which includes an RGB branch, a Hematoxylin branch and a Segmentation branch. Then we propose a novel feature aggregation strategy to allow the network to fuse features progressively and to learn better feature representations from different branches. Extensive experiments are performed to qualitatively and quantitatively evaluate the effectiveness of our proposed method. In the meanwhile, it outperforms state-of-the-art methods on three different nuclei segmentation datasets.

Disparity in clinical outcomes between pure and combined pulmonary large-cell neuroendocrine carcinoma: A multi-center retrospective study.

OBJECTIVES: The 2015 World Health Organization classification defines pulmonary large-cell neuroendocrine carcinoma (LCNEC) as a high-grade neuroendocrine carcinoma. However, the clinical characteristics and prognostic factors of pure LCNEC and combined LCNEC remain unclear. Hence, we performed a multi-center retrospective study to compare the clinical outcomes of pure versus combined LCNEC. MATERIALS AND METHODS: Data from 381 patients with pulmonary LCNEC admitted to 17 Chinese institutes between 2009 and 2016 were collected retrospectively. Clinical characteristics and prognosis were analyzed among patients receiving adjuvant (adjuvant group; n = 56) and first-line (first-line group; n = 146) chemotherapy, as well as among patients receiving small cell lung cancer (SCLC) and non-SCLC (NSCLC) chemotherapy regimens. The Kaplan-Meier method and multivariable Cox regression were used to identify clinicopathological variables that might influence patient outcomes. RESULTS: Expression levels of neuroendocrine markers (synaptophysin, chromogranin-A, CD56) were associated with patients' prognosis in the total study cohort. In the adjuvant group, median disease-free survival was non-significantly longer for SCLC-based regimens than for NSCLC-based regimens (P = 0.112). In the first-line group, median progression-free survival was significantly longer for SCLC-based regimens than for NSCLC-based regimens (11.5 vs. 7.2 months, P = 0.003). Among patients with combined LCNEC, adenocarcinoma was the most common combined component, accounting for 70.0 % of cases. Additionally, median overall survival was non-significantly shorter for combined LCNEC than for pure LCNEC (P = 0.083). CONCLUSION: The SCLC regimen is a more effective choice, as either first-line or adjuvant chemotherapy, when compared to the NSCLC regimen for LCNEC treatment. Further studies are needed to clarify the survival differences between patients with pure-, and combined LCNEC.