Clinical and radiological characteristics of central pulmonary adenocarcinoma: a comparison with central squamous cell carcinoma and small cell lung cancer and the impact on treatment response

Wang, Zhe; Li, Minghuan; Huang, Yong; Ma, Li; Zhu, Hui; Kong, Li; J, Yu

doi:10.2147/ott.s154385

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…While there were significant differences in OS and RFS between the groups before PSM, there were also significant differences in the baseline characteristics of patients between the groups. BB is more suitable for the diagnosis of central lung cancer [ 25 ], so the BB group was mostly male patients with squamous carcinoma who smoked, which may lead to different postoperative adjuvant therapies and prognosis in these patients than in the non-biopsy group [ 26 ]. Similarly, the selection bias exists for whether to perform PNB on patients preoperatively.…”

Section: Discussionmentioning

confidence: 99%

Impact of preoperative biopsy on tumor spread through air spaces in stage I non-small cell lung cancer: a propensity score-matched study

Ding

et al. 2022

BMC Pulm Med

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Background Percutaneous needle biopsy (PNB) and bronchoscopic biopsy (BB) are widely used in the preoperative diagnosis of pulmonary nodules, but whether PNB or BB may cause tumor spread through air spaces (STAS) has not been reported. Methods 433 postoperative patients with pathological stage I non-small cell lung cancer (NSCLC) from January 2015 to December 2018 at our hospital were enrolled and divided into PNB group (n = 40), BB group (n = 48) and non-biopsy group (n = 345). The PNB and BB groups were matched using propensity score matched (PSM) separately from the non-biopsy group, after which the effects of PNB and BB on STAS, recurrence-free survival (RFS) and overall survival (OS) were assessed. Results After PSM for 9 confounding factors (gender, age, smoking history, tumor site, scope of surgery, pathology type, stage, maximum tumor diameter and postoperative treatment), 38 cases in the PNB group were successfully matched with 38 cases in the non-biopsy group and 28 cases in the BB group were successfully matched with 28 cases in the non-biopsy group. After PSM, there was no significant difference in the incidence of STAS between the PNB and non-biopsy groups (42.1% vs. 34.2%, P > 0.05) and between the BB and non-biopsy groups (42.9% vs. 46.4%, P > 0.05). The results after PSM showed no significant effect of both PNB and BB on RFS and OS after radical surgery (P > 0.05). Conclusion Preoperative biopsy in patients with stage I NSCLC has not been shown to increase the occurrence of STAS, nor postoperative recurrence and death.

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Section: Discussionmentioning

confidence: 99%

Impact of preoperative biopsy on tumor spread through air spaces in stage I non-small cell lung cancer: a propensity score-matched study

Ding

et al. 2022

BMC Pulm Med

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“…There is a difference in genetic landscape between centrally and peripherally located NSCLC that is affected by exposure to various environmental carcinogens [ 77 ]. For instance, exposure to tobacco carcinogens leads to centrally located NSCLC tumors with higher accumulation of alterations in suppressor genes [ 78 ]. On the other hand, oncogenic alterations such as EGFR-activating mutations are prone to occur in the peripheral location that is more common to LUAD [ 79 , 80 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…Between 80% to 85% of lung cancers are non-small cell lung cancer (NSCLC), and the most prevalent subtypes are adenocarcinoma (ADC) and squamous cell carcinoma (SCC) [2,3]. ADC is the most commonly diagnosed type of lung cancer and frequently occurs along the outer periphery of the lung [4,5]. SCC comprises 25-30% of lung cancer cases and usually occurs in the central portion of the lung as well as being considered more aggressive than ADC [4].…”

Section: Introductionmentioning

confidence: 99%

“…ADC is the most commonly diagnosed type of lung cancer and frequently occurs along the outer periphery of the lung [4,5]. SCC comprises 25-30% of lung cancer cases and usually occurs in the central portion of the lung as well as being considered more aggressive than ADC [4]. Other clinicopathologic differences are highlighted in Table S1.…”

Section: Introductionmentioning

confidence: 99%

Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results

Alvarez-Jimenez

Sandino

Prasanna

et al. 2020

Cancers

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(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.

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Clinical and radiological characteristics of central pulmonary adenocarcinoma: a comparison with central squamous cell carcinoma and small cell lung cancer and the impact on treatment response

Cited by 13 publications

References 39 publications

Impact of preoperative biopsy on tumor spread through air spaces in stage I non-small cell lung cancer: a propensity score-matched study

Impact of preoperative biopsy on tumor spread through air spaces in stage I non-small cell lung cancer: a propensity score-matched study

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

Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results

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