Differing histopathology and prognosis in pulmonary adenocarcinoma at central and peripheral locations

Development and validation of a predictive model for lymph node metastases in peripheral non-small cell lung cancer with a tumor diameter ≤ 2.0 cm and a consolidation-to-tumor ratio > 0.5

Background

Precisely predicting lymph node metastasis (LNM) status is critical for the treatment of early non-small5-cell lung cancer (NSCLC). In this study, we developed a LNM prediction tool for peripheral NSCLC with a tumor diameter ≤ 2.0 cm and consolidation-to-tumor ratio (CTR) > 0.5 to identify patients where segmentectomy could be applied.

Methods

Clinical characteristics were retrospectively collected from 435 patients with NSCLC. Logistic regression analysis of the clinical characteristics of this development cohort was used to estimate independent LNM predictors. A prediction model was then developed and externally validated using a validation cohort at another institution.

Results

Four independent predictors (tumor size, CTR, pleural indentation, and carcinoembryonic antigen (CEA) values) were identified and entered into the model. The model showed good calibration (Hosmer-Lemeshow (HL) P value = 0.680) with an area under the receiver operating characteristic curve (AUC) = 0.890 (95% confidence interval (CI): 0.808-0.972) in the validation cohort.

Conclusions

We developed and validated a novel and effective model that predicted the probability of LNM for individual patients with peripheral NSCLC who had a tumor diameter ≤ 2.0 cm and CTR > 0.5. This model could help clinicians make individualized clinical decisions.

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

BACKGROUND

Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice.

METHODS

Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations.

RESULTS

We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification.

CONCLUSIONS

We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

Characteristics and Prognostic Nomogram for Primary Lung Lepidic Adenocarcinoma

Background

Lepidic adenocarcinoma (LPA) is an infrequent subtype of invasive pulmonary adenocarcinoma (ADC). However, the clinicopathological features and prognostic factors of LPA have not been elucidated.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) database of 4191 LPA patients were retrospectively analyzed and compared with non-LPA pulmonary ADC to explore the clinicopathological and prognosis features of LPA. Univariate and multivariate Cox proportional hazard models were performed to identify independent survival predictors for further nomogram development. The nomograms were validated using the concordance index, receiver operating characteristic curves, and calibration plots, as well as decision curve analysis, in both the training and validation cohorts.

Results

Compared with non-LPA pulmonary ADC patients, those with LPA exhibited unique clinicopathological features, including more elderly and female patients, smaller tumor size, less pleural invasion, and lower histological grade and stage. Multivariate analyses showed that age, sex, race, tumor location, primary tumor size, pleural invasion, histological grade, stage, primary tumor surgery, and chemotherapy were independently associated with overall survival (OS) and cancer-specific survival (CSS) in patients with LPA. The nomograms showed good accuracy compared with the actual observed results and demonstrated improved prognostic capacity compared with the TNM stage.

Conclusions

LPA is more frequently diagnosed in older people and women. LPA was inclined to be smaller in tumor size and lower in tumor grade and staging, which may indicate a favorable prognosis. The constructed nomograms accurately predict the long-term survival of LPA patients.

Bone-Specific Metastasis Pattern of Advanced-Stage Lung Adenocarcinoma According to the Localization of the Primary Tumor

Background: Patients with advanced-stage lung adenocarcinoma (LADC) often develop distant metastases in the skeletal system. Yet, the bone-specific metastasis pattern is still controversial. We, therefore, aimed to examine how the primary tumor location affects bone specificity and survival in LADC patients diagnosed with skeletal metastases. Methods: In total, 209 bone-metastatic Caucasian LADC patients from two thoracic centers were included in this study. Focusing on the specific location of primary tumors and bone metastatic sites, clinicopathological variables were included in a common database and analyzed retrospectively. Skeletal metastases were diagnosed according to the contemporary diagnostic guidelines and confirmed by bone scintigraphy. Besides region- and side-specific localization, primary tumors were also classified as central or peripheral tumors based on their bronchoscopic visibility. Results: The most common sites for metastasis were the spine (n = 103) and the ribs (n = 60), followed by the pelvis (n = 36) and the femur (n = 22). Importantly, femoral (p = 0.022) and rib (p = 0.012) metastases were more frequently associated with peripheral tumors, whereas centrally located LADCs were associated with humeral metastases (p = 0.018). Moreover, we deduced that left-sided tumors give rise to skull metastases more often than right-sided primary tumors (p = 0.018). Of note, however, the localization of the primary tumor did not significantly influence the type of affected bones. Multivariate Cox regression analysis adjusted for clinical parameters demonstrated that central localization of the primary tumor was an independent negative prognostic factor for overall survival (OS). Additionally, as expected, both chemotherapy and bisphosphonate therapy conferred a significant benefit for OS. Conclusion: The present study demonstrates unique bone-specific metastasis patterns concerning primary tumor location. Peripherally located LADCs are associated with rib and femoral metastases and improved survival outcomes. Our findings might contribute to the development of individualized follow-up strategies in bone-metastatic LADC patients and warrant further clinical investigations on a larger sample size.

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

Background

Based on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).

Methods

Radiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.

Results

Thirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.

Conclusion

CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

The promises and challenges of early non-small cell lung cancer detection: patient perceptions, low-dose CT screening, bronchoscopy and biomarkers

Lung cancer survival statistics are sobering with survival ranking among the poorest of all cancers despite the addition of targeted therapies and immunotherapies. However, improvements in tools for early detection hold promise. The Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) trial recently corroborated the findings from the previous National Lung Screening Trial low‐dose Computerised Tomography (NLST) screening trial in reducing lung cancer mortality. Biomarker research and development is increasing at pace as the molecular life histories of lung cancers become further unravelled. Low‐dose CT screening (LDCT) is effective but targets only those at the highest risk and is burdensome on healthcare. An optimally designed CT screening programme at best will only detect a low proportion of overall lung cancers as only those at very high‐risk meet screening criteria. Biomarkers that help risk stratify suitable patients for LDCT screening, and those that assist in determining which LDCT detected nodules are likely to represent malignant disease are needed. Some biomarkers have been proposed as standalone lung cancer diagnosis tools. Bronchoscopy technology is improving, with better capacity to identify and obtain samples from early lung cancers. Clinicians need to be aware of each early lung cancer detection method’s inherent limitations. We anticipate that the future of early lung cancer diagnosis will involve a synergistic, multimodal approach, combining several early detection methods.

Worse lung cancer outcome in patients with lower respiratory tract infection confirmed at time of diagnosis

Background

Pulmonary malignancy is one of the most frequent and fatal cancers in older patients. As data on lower respiratory tract infection (LRTI) and the outcome of lung cancer are scarce, our objective was to determine the impact of LRTI on therapeutic possibilities and one‐year mortality.

Methods

Patients undergoing bronchoscopy in 2017 who had bronchial microbial sampling at the time of the lung cancer diagnosis (n = 143) were included. Group 1 (LRTI+) included patients with confirmed infection (n = 74) while Group 2 (LRTI‐) included patients without infection (n = 69). Clinical characteristics, pathogen profile and one‐year survival were analyzed.

Results

Age, gender, TNM stage, histology type, comorbidities or underlying lung disease did not differ among groups. The most common LRTI pathogens included aerobic (n = 49), anaerobic (n = 14) and fungal (n = 26) infections. Chemo/immune/target therapy alone, or in combination with radiotherapy were significantly less frequently used, whilst palliative care was more common in Group 1 (LRTI+). Multiple pathogen LRTI patients were significantly older, less frequently diagnosed with adenocarcinoma and had worse performance status compared to solitary pathogen LRTI patients. One‐year median survival was 274 days (235 vs. 305 days Group 1 vs. Group 2). Risk factors for increased one‐year mortality included performance status ≥2 (OR 30.00, CI 95% 5.23–313.00), performance status 1 (OR 11.87, CI 95% 4.12–33.78), male gender (OR 4.04, CI 2.03–8.04), LRTI with multiple pathogens (OR 2.72, CI 1.01–6.81) and nonadenocarcinoma histology (OR 2.26, CI 1.15–4.56).

Conclusion

LRTIs in lung cancer patients, especially multiple pathogen infections, are associated with less oncotherapeutic possibilities and significant risk for lower one‐year median survival.

Prognostic Factors of Pathological N1 Non-small Cell Lung Cancer After Curative Resection Without Adjuvant Chemotherapy

BACKGROUND

The aim of this study was to evaluate the outcomes of patients with pathological N1 non-small cell lung cancer who did not receive adjuvant chemotherapy. We attempted to identify those patients for whom adjuvant chemotherapy would be indispensable.

METHODS

Among 132 patients who were diagnosed with pathological N1 lung cancer at a single institution from January 2010 to December 2016 were 32 patients who did not receive adjuvant treatment after curative surgical resection. The surgical and oncological outcomes of these patients were analyzed. Candidate factors for predicting recurrence were analyzed to identify patients at high risk of recurrence.

RESULTS

The median follow-up time for all 32 patients was 1044 days. The 5-year recurrence-free survival (RFS) and disease-specific survival rates of the patients without adjuvant therapy were 50.3% and 77.6%, respectively. By multivariate analysis, tumors with a lepidic growth pattern [hazard ratio (HR) 0.119, p = 0.024] and extralobar lymph node metastasis (HR 6.848, p = 0.015) were significant factors predicting recurrence. The difference between the 5-year RFS rates of patients with tumors with or without a lepidic growth pattern was statistically significant (63.5% vs 40.0%, respectively; p = 0.050). The 5-year RFS rates of patients with intralobar lymph node metastasis versus those with extralobar lymph node metastasis were 63.3% and 18.8%, respectively (p = 0.002).

CONCLUSIONS

Patients with tumors without a lepidic growth pattern or with extralobar lymph node metastasis who do not receive adjuvant chemotherapy have a high recurrence rate after surgery. Therefore, these patients should be encouraged to undergo adjuvant chemotherapy if their overall condition is not a contraindication for chemotherapy.

Prognosis of upstaged N1 and N2 disease after curative resection in patients with clinical N0 non-small cell lung cancer

Background

Nodal upstaging occasionally occurs after curative resection in clinical N0 non-small cell lung cancer (NSCLC). The purpose of this study was to evaluate the prognosis of clinical N0 NSCLC (T1-2, tumor size 5 cm or smaller) after upstaging to pathologic N1 or N2.

Methods

From 2005 to 2015, 676 consecutive patients were diagnosed with clinical T1-2N0 NSCLC and underwent curative resection. Among these, tumors were upstaged to N1 in 46 patients and to N2 in 24 patients. We analyzed the prognosis of upstaged tumors. For comparison of prognosis between nodal upstaging groups and others in the same stage, patients with preoperative pathologically proven N1 (n=31) and N2 (n=55) NSCLC were included in the study.

Results

A total of 70 patients (10.4%) had nodal upstaging after curative resection of clinical N0 NSCLC. Upstaging to N1 occurred in 46 patients and upstaging to N2 occurred in 24 patients. The 5-year disease-specific survival rate was not statistically different between the upstaged and non-upstaged N1/N2 groups in N1 disease (73.3% vs. 70.5%, P=0.247) or in N2 disease (58.9% vs. 50.7%, P=0.283). Multivariate analysis showed that nodal upstaging was not a significant prognostic factor in N1 or N2 NSCLC (hazard ratio =0.385, P=0.235; hazard ratio =0.677, P=0.458).

Conclusions

Postoperative nodal upstaging from clinical T1-2N0 NSCLC was not a significant prognostic factor in the same stage. Therefore, surgical treatment of clinical T1-2N0 lung cancer diagnosed by imaging without preoperative pathologic lymph node staging can be a treatment option.

Prognostic Factors in Stage IIB Non-Small Cell Lung Cancer according to the 8th Edition of TNM Staging System

Background

The purposes of this study were to evaluate the appropriateness of the stage migration of stage IIA non-small cell lung cancer (NSCLC) in the seventh edition of the tumor, node, and metastasis classification for lung cancer to stage IIB lung cancer in the eighth edition, and to identify prognostic factors in patients with eighth-edition stage IIB disease.

Methods

Patients with eighth-edition stage IIB disease were subclassified into those with seventh-edition stage IIA disease and those with seventh-edition stage IIB disease, and their recurrence-free survival and disease-specific survival rates were compared. Risk factors for recurrence after curative resection were identified in all included patients.

Results

Of 122 patients with eighth-edition stage IIB NSCLC, 101 (82.8%) had seventh-edition stage IIA disease and 21 (17.2%) had seventh-edition stage IIB disease. Nonsignificant differences were observed in the 5-year recurrence-free survival rate and the 5-year disease-specific survival rate between the patients with seventh-edition stage IIA disease and those with seventh-edition stage IIB disease. Visceral pleural invasion was a significant risk factor for recurrence in patients with eighth-edition stage IIB NSCLC.

Conclusion

The stage migration from seventh-edition stage IIA NSCLC to eighth-edition stage IIB NSCLC was appropriate in terms of oncological outcomes. Visceral pleural invasion was the only prognostic factor in patients with eighth-edition stage IIB NSCLC.

Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?

To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

Main bronchus location is a predictor for metastasis and prognosis in lung adenocarcinoma: A large cohort analysis

OBJECTIVES

In the literature, inconsistent associations between the primary locations of lung adenocarcinomas (ADCs) with patient prognosis have been reported, due to varying definitions for central and peripheral locations. In this study, we investigated the clinical characteristics and prognoses of ADCs located in the main bronchus.

METHODS

A total of 397,189 lung ADCs registered from 2004 to 2013 in the National Cancer Database (NCDB) were extracted and divided into main bronchus-located ADCs (2.5%, N = 10,111) and non-main bronchus ADCs (97.5%, N = 387,078). The ADCs located in the main bronchus and those not in the main bronchus were compared in terms of patient prognosis, lymph node involvement, distant metastases and other clinical features, including rate of curative-intent resection, histologic grade, and stage.

RESULTS

ADCs located in the main bronchus had significantly worse patient survival than those in the non-main bronchus, both for all patients (HR = 1.82, 95% CI 1.78-1.86) and for those undergoing curative-intent resection (HR = 2.49, 95% CI 2.23-2.78). Furthermore, ADCs located in the main bronchus had a significantly higher rate of lymph node involvement and distant metastasis than those not in the main bronchus, when stratified by tumor size (trend test, p < e^-16). Multivariate analysis of overall survival showed that main bronchus location is a prognostic factor (HR = 1.15, 95% CI 1.08-1.23) independent of other clinical factors.

CONCLUSIONS

Main bronchus location is an independent predictor for metastasis and worse outcomes irrespective of stage and treatment. Tumor primary location might be considered in prognostication and treatment planning.