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.

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.

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.

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.

Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer.

OBJECTIVE: To develop and validate a radiomics-based predictive risk score (RPRS) for preoperative prediction of lymph node (LN) metastasis in patients with resectable non-small cell lung cancer (NSCLC). METHODS: We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016. By using the method of radiomics analysis, 591 computed tomography (CT)-based radiomics features were extracted, and the radiomics-based classifier was constructed. Then, using multivariable logistic regression analysis, a weighted score RPRS was derived to identify LN metastasis. Apparent prediction performance of RPRS was assessed with its calibration, discrimination, and clinical usefulness. RESULTS: The radiomics-based classifier was constructed, which consisted of 13 selected radiomics features. Multivariate models demonstrated that radiomics-based classifier, age group, tumor diameter, tumor location, and CT-based LN status were independent predictors. When we assigned the corresponding score to each variable, patients with RPRSs of 0-3, 4-5, 6, 7-8, and 9 had distinctly very low (0%-20%), low (21%-40%), intermediate (41%-60%), high (61%-80%), and very high (81%-100%) risks of LN involvement, respectively. The developed RPRS showed good discrimination and satisfactory calibration [C-index: 0.785, 95% confidence interval (95% CI): 0.780-0.790]. Additionally, RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index (NRI) of 0.711 (95% CI: 0.555-0.867). CONCLUSIONS: The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC. This stratification of patients according to their LN status may provide a basis for individualized treatment.

VISTA expression associated with CD8 confers a favorable immune microenvironment and better overall survival in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) often arises in the setting of chronic inflammation with multiple inhibitory immune signals. V-domain Ig suppressor of T cell activation (VISTA) is identified as a novel negative checkpoint regulator. This study sought to determine the expression and prognostic value of VISTA in HCC and classify tumor microenvironments (TMEs) based on VISTA and CD8+ tumor-infiltrating lymphocytes (TILs). METHODS: The expression of VISTA and CD8 proteins was assessed in 183 HCC tissue microarrays (TMAs) by immunohistochemistry (IHC). VISTA and CD8A mRNA extracted from 372 patients with HCC in The Cancer Genome Atlas (TCGA) database was included as a validation cohort. Associations between the VISTA, clinicopathological variables, and survival were analyzed. RESULTS: VISTA expression was detected in 29.5% HCC tissues, among which 16.4% tissues were positive for tumor cells (TCs), and 16.9% tissues were positive for immune cells (ICs). VISTA expression was significantly associated with tissues with a high pathological grading (p = 0.038), without liver cirrhosis (p = 0.011), and with a high density of CD8 + TILs (p < 0.001). Kaplan-Meier curves demonstrated that patients with VISTA-positive staining in TCs (p = 0.037), but not in ICs, (p = 0.779) showed significantly prolonged overall survival (OS) than those with VISTA-negative expression. Classification of HCC TME-based VISTA and CD8 + TILs showed 4 immune subtypes: VISTA+/CD8+ (16.9%), VISTA+/CD8- (12.6%), VISTA-/CD8+ (16.4%), and VISTA-/CD8+ (54.1%). The dual positive VISTA+/CD8+ subtype showed significantly prolonged OS than other subtypes (p = 0.023). CONCLUSIONS: VISTA protein expression in HCC showed cell specific and displayed different prognosis. VISTA expression was significantly associated with CD8 + TILs, Dual positive VISTA+/CD8+ showed favorable TME and better OS.

Intrahepatic cholangiocarcinoma prognostic determination using pre-operative serum C-reactive protein levels.

BACKGROUND: Serum C-reactive protein (CRP), an acute inflammatory response biomarker, has been recognized as an indicator of malignant disease progression. However, the prognostic significance of CRP levels collected before tumor removal in intrahepatic cholangiocarcinoma requires further investigation. METHODS: We sampled the CRP levels in 140 patients with intrahepatic cholangiocarcinoma who underwent hepatectomies with regional lymphadenectomies between 2006 and 2013. A retrospective analysis of the clinicopathological data was performed. We focused on the impact of serum CRP on the patients' cancer-specific survival and recurrence-free survival rates. RESULTS: High levels of preoperative serum CRP were significantly associated with well-established clinicopathologic features, including gender, advanced tumor stage, and elevated carcinoembryonic antigen and carbohydrate antigen 19-9 levels (P < 0.05). Univariate analysis demonstrated a significant association between high levels of serum CRP and adverse cancer-specific survival (P = 0.001) and recurrence-free survival (P < 0.001). In patients with stage I/II intrahepatic cholangiocarcinoma, the serum CRP level was a prognostic indicator for cancer-specific survival. In patients with stage I/II or stage III/IV, the serum CRP level was a prognostic indicator for recurrence-free survival (P < 0.05). Additionally, multivariate analysis identified serum CRP level in intrahepatic cholangiocarcinoma as an independent prognostic factor (P < 0.05). CONCLUSIONS: We confirmed a significant association of elevated pre-operative CRP levels with poor clinical outcomes for the tested patients with intrahepatic cholangiocarcinoma. Our results indicate that the serum CRP level may represent a useful factor for patient stratification in intrahepatic cholangiocarcinoma management.

[Detection of KRAS,NRAS and BRAF gene mutations in colorectal carcinoma].

OBJECTIVE: To investigate mutations frequencies of KRAS,NRAS and BRAF genes in colorectal carcinoma. METHODS: Tissue specimens from 200 colorectal cancer patients at diagnosis were collected and subject to KRAS,NRAS and BRAF mutation analyses by PCR-based direct DNA sequencing targeting exons 2, 3 and 4 of KRAS gene, exons 2, 3 and 4 of NRAS gene and exon 15 of BRAF gene. RESULTS: Activating mutations were detected in KRAS (44%, 88/200), NRAS (2%, 4/200) and BRAF (5%, 10/200) in this study cohort.Among KRAS mutations, 64.8% (57/88) occurred in codon 12 and 12.5% (11/88) occurred in codon 13. KRAS gene mutation in exon 3 mainly involved codons 59 and 61. KRAS gene mutation in exon 4 mainly involved codons 117 and 146. CONCLUSIONS: Mutations at exon 2 of KRAS gene have the highest frequency in colorectal carcinoma. Expanding the detection sites of KRAS gene combined with NRAS and BRAF genes may help to identify patients who will most likely benefit from targeted therapies.

Profiling plasma microRNA in nasopharyngeal carcinoma with deep sequencing.

BACKGROUND: The goal of this study was to establish a plasma microRNA profile by use of next-generation sequencing that could aid in assessment of patient prognosis in nasopharyngeal carcinoma (NPC). METHODS: Two panels of NPC patients and healthy controls (HCs) were recruited for this study. We used deep sequencing to screen plasma microRNAs. Differentially expressed microRNAs were verified by quantitative real-time PCR (qPCR). Kaplan-Meier survival analysis with the log-rank test was used to compare overall survival (OS) and progression-free survival (PFS) between groups. RESULTS: Twenty-three plasma miRNAs with differential expression levels were selected for qPCR analysis on an independent set including 100 NPC patients and 55 HCs. NPC patients with low concentrations of miR-483-5p and miR-103 had better prognosis for 5-year OS than those with high concentrations (87.5% vs 55.8%, P < 0.001; 80.9% vs 62.3%, P = 0.031). Those with low concentrations of miR-29a and let-7c had poorer prognosis (54.8% vs 82.8%, P = 0.002; 56.3% vs 84.6%, P = 0.001). A 3-signature miRNA integrated with clinical stage was further identified in an independent set. We calculated a prognostic index score and classified patients into low-, medium-, and high-risk groups. Five-year OS among the 3 groups was significantly different (90.9%, 66.7%, and 23.8%; P < 0.001). By multivariate analysis, a high-risk score was the most significantly unfavorable prognostic factor independent of other clinical variables (P < 0.001, hazard ratio = 15.1, 95% CI = 5.2-43.9). CONCLUSIONS: Differentially expressed plasma miRNAs as identified by next-generation sequencing can be helpful for predicting survival in NPC patients.

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.

[B-cell lymphomas with concurrent myc and bcl-2/IgH or bcl-6 translocations].

OBJECTIVE: To identify and investigate clinicopathological features of B cell lymphomas with concurrent myc and bcl-2/IgH or bcl-6 translocations ("double-hit" lymphoma). METHODS: Tissue microarray was constructed from formalin-fixed and paraffin-embedded tissue samples of aggressive B cell lymphomas diagnosed between 2009 and 2012, including 129 cases of diffuse large B cell lymphoma (DLBCL), 5 cases of B-cell lymphoma, unclassifiable with features intermediate between diffuse large B-cell lymphoma and Burkitt lymphoma (BCLU), 7 cases of Burkitt lymphoma and 4 cases of high-grade follicular lymphoma with diffuse large B cell lymphoma component. Interphase fluorescence in-situ hybridization (FISH) was performed with a panel of probes including myc, bcl-2/IgH and bcl-6 to document related gene translocation and copy number changes. Medical record review was performed and follow-up data was recorded. RESULTS: Among 145 cases, 5 cases (3.4%) of B cell lymphomas with concurrent myc and bcl-2/IgH or bcl-6 rearrangements (double-hit lymphomas) were identified, including 2 cases involving myc and bcl-2 translocations (1 DLBCL and 1 BCLU), and 3 cases involving myc and bcl-6 translocations (all DLBCLs). Three cases with concurrent bcl-2/IgH and bcl-6 translocations were found. Single gene translocations or increase of copy numbers were found in 66 cases, representing 51.2% (66/129) of all de novo DLBCLs. Ki-67 index of the 5 "double-hit" lymphomas ranged from 60% to 100%. Clinical follow-up data were available in 4 of the 5 "double-hit" lymphoma patients, three of whom died within 2 years and 1 patient was alive after 36 months of follow-up. CONCLUSIONS: "Double-hit" B-cell lymphomas are rare and can only be identified by molecular detection. They should not be considered synonymous with BCLU morphologically, and may present entities within other morphological spectra. Most of the patients have a poor prognosis. Further in-depth studies of larger case numbers are required to determine the pathologic and genetic variables of the lesion.

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.

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.

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).

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.

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.

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.

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.

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.

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.