Adenocarcinoma containing lepidic growth

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Number of involved nodal stations: a better lymph node classification for clinical stage IA lung adenocarcinoma

Background

With the popularization of lung cancer screening, more early-stage lung cancers are being detected. This study aims to compare three types of N classifications, including location-based N classification (pathologic nodal classification [pN]), the number of lymph node stations (nS)-based N classification (nS classification), and the combined approach proposed by the International Association for the Study of Lung Cancer (IASLC) which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer.

Methods

We retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2005 and 2018. Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period. Sub-analyses were performed for the three types of N classifications. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications. The prediction performance among the three types of N classifications was compared using the concordance index (C-index) and decision curve analysis (DCA).

Results

Of the 669 patients evaluated, 534 had pathological stage N0 disease (79.8%), 82 had N1 disease (12.3%) and 53 had N2 disease (7.9%). Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis (all P < 0.001). However, the prognosis overlaps between pN (N1 and N2, P = 0.052) and IASLC-proposed N classification (N1b and N2a1 [P = 0.407], N2a1 and N2a2 [P = 0.364], and N2a2 and N2b [P = 0.779]), except for nS classification subgroups (nS0 and nS1 [P < 0.001] and nS1 and nS >1 [P = 0.006]). There was no significant difference in the C-index values between the three N classifications (P = 0.370). The DCA results demonstrated that the nS classification provided greater clinical utility.

Conclusion

The nS classification might be a better choice for nodal classification in clinical stage IA lung adenocarcinoma.

Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods

Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a K-means algorithm and the resultant partition, defined by the cluster centroids, is herein named the noiselet code-book. A part of an image, a tile, is divided into patches and represented by the histogram of occurrences of the projected patches in the noiselet code-book. Finally, with these histograms, a classifier learns to differentiate between nuclei and non-nuclei tiles. By applying a conventional watershed-marked method to detect and segment nuclei, evaluation consisted in comparing pure watershed method against denoising-plus-watershed in an open database with 8 different types of tissues. The averaged F-score of nuclei detection improved from 0.830 to 0.86 and the dice score after segmentation increased from 0.701 to 0.723.

Predictors of Invasiveness in Adenocarcinoma of Lung with Lepidic Growth Pattern

Lung adenocarcinoma with lepidic growth pattern (LPA) is characterized by tumor cell proliferation along intact alveolar walls, and further classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive lepidic predominant adenocarcinoma (iLPA). Accurate diagnosis of lepidic lesions is critical for appropriate prognostication and management as five-year survival in patients with iLPA is lower than in those with AIS and MIA. We aimed to evaluate the accuracy of CT-guided core needle lung biopsy classifying LPA lesions and identify clinical and radiologic predictors of invasive disease in biopsied lesions. Thirty-four cases of adenocarcinoma with non-invasive lepidic growth pattern on core biopsy pathology that subsequently were resected between 2011 and 2018 were identified. Invasive LPA vs. non-invasive LPA (AIS or MIA) was defined based on explant pathology. Histopathology of core biopsy and resected tumor specimens was compared for concordance, and clinical, radiologic and pathologic variables were analyzed to assess for correlation with invasive disease. The majority of explanted tumors (70.6%) revealed invasive disease. Asian race (p = 0.03), history of extrathoracic malignancy (p = 0.02) and absence of smoking history (p = 0.03) were associated with invasive disease. CT-measured tumor size was not associated with invasiveness (p = 0.15). CT appearance of density (p = 0.61), shape (p = 0.78), and margin (p = 0.24) did not demonstrate a significant difference between the two subgroups. Invasiveness of tumors with lepidic growth patterns can be underestimated on transthoracic core needle biopsies. Asian race, absence of smoking, and history of extrathoracic malignancy were associated with invasive disease.

CEA-Ki-67- Pathologic Subtype: An Adjunct Factor for Refining Prognosis in Stage I Pulmonary Adenocarcinoma

Objectives

The prognosis for stage I pulmonary adenocarcinoma is generally good. However, some patients with stage I pulmonary adenocarcinoma have an unexpectedly poor outcome. This warrants consideration of adjunct markers. In this study, we analyze carcinoembryonic antigen, Ki-67, and a pathologic subtype in combination for prognostic evaluation of stage I pulmonary adenocarcinoma. These factors were selected for study as they have been shown to be individually associated with prognosis in many studies.

Methods

A total of 650 patients with stage I pulmonary adenocarcinoma were investigated retrospectively. Each patient was re-staged using standard TNM criteria. Carcinoembryonic antigen (CEA) values were obtained from preoperative blood samples, and Ki-67 was evaluated with tumor tissue immunohistochemistry. Patient clinicopathologic characteristics, survival status, and date of death were obtained from medical records and telephone follow-up.

Results

CEA > 4.4 ng/ml, Ki-67 > 13%, and a solid-micropapillary tumor growth pattern were each independent adverse prognostic markers for 5-year disease specific survival in stage I pulmonary adenocarcinoma. However, in combination, these 3 factors yielded a prognostic value (designated “CEA-Ki-67-pathologic subtype” value). Stage I pulmonary adenocarcinoma of low-risk CEA-Ki-67-pathologic subtype (CKP) value show biologic behavior similar to TNM stage IA1 tumors, while stage I tumors of high-risk CKP value are similar in prognosis to TNM stage II.

Conclusion

The CKP value may be used as an adjunct to the TNM classification, which may yield a more accurately defined prognosis for cases of stage I pulmonary adenocarcinoma. CKP value may identify patients at higher risk who may benefit from adjuvant chemotherapy. Conversely, lower risk CKP values may support avoidance of chemotherapy.