Reproducibility of histopathological diagnosis in poorly differentiated NSCLC: an international multiobserver study

Nanotechnology-based approaches overcome lung cancer drug resistance through diagnosis and treatment

Lung cancer continues to be a malignant tumor with high mortality. Two obstacles interfere with curative therapy of lung cancer: (i) poor diagnosis at the early stages, as symptoms are not specific or asymptomatic; and (ii) invariably emerging drug resistance after treatment. Some factors contributing to drug resistance include preexisting genetic/genomic drug-resistant alteration(s); activation of adaptive drug resistance pathways; remodeling of the tumor microenvironment; and pharmacological mechanisms or activation of drug efflux pumps. Despite the mechanisms explored to better understand drug resistance, a gap remains between molecular understanding and clinical application. Therefore, facilitating the translation of basic science into the clinical setting is a great challenge. Nanomedicine has emerged as a promising tool for cancer treatment. Because of their excellent physicochemical properties and enhanced permeability and retention effects, nanoparticles have great potential to revolutionize conventional lung cancer diagnosis and combat drug resistance. Nanoplatforms can be designed as carriers to improve treatment efficacy and deliver multiple drugs in one system, facilitating combination treatment to overcome drug resistance. In this review, we describe the difficulties in lung cancer treatment and review recent research progress on nanoplatforms aimed at early diagnosis and lung cancer treatment. Finally, future perspectives and challenges of nanomedicine are also discussed.

Chemokines and NSCLC: Emerging role in prognosis, heterogeneity, and therapeutics

Lung cancer persists to contribute to one-quarter of cancer-associated deaths. Among the different histologies, non-small cell lung cancer (NSCLC) alone accounts for 85% of the cases. The development of therapies involving immune checkpoint inhibitors and angiogenesis inhibitors has increased patients' survival probability and reduced mortality rates. Developing targeted therapies against essential genetic alterations also translates to better treatment strategies. But the benefits still seem farfetched due to the development of drug resistance and refractory tumors. In this review, we have highlighted the interplay of different tumor microenvironment components, essentially discussing the chemokine families (CC, CXC, C, and CX3C) that regulate the tumor biology in NSCLC and promote tumor growth, metastasis, and associated heterogeneity. The development of therapeutics and prognostic markers is a complex and multipronged approach. However, some essential chemokines can act as critical players for being considered potential prognostic markers and therapeutic targets.

The Diagnostic and Prognostic Roles of Combined Expression of Novel Biomarkers in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma: An Immunohistochemical Study

Background & Objective:

Diagnosis and discrimination of lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC) is critical to select the appropriate treatment regimen as recently targeted therapies require accurate subtyping of nonsmall-cell lung carcinoma (NSCLCs). There are currently several biomarkers that could be used for differentiation between LUAD and LUSC, but they have less sensitivity, specificity, and clinical applicability. The aim of this study was to assess the diagnostic and prognostic values of CLCA2, SPATS2, ST6GALNAC1, and Adipophilin tissue expression in the tissues retrieved from LUAD and LUSC patients using immunohistochemistry.

Methods:

The current study was performed on the samples retrieved from sixty primary lung masses that were diagnosed as LUAD and LUSC. Immunohistochemistry was performed by using a panel of CLCA2, SPATS2, and ST6GALNAC1. We assessed the diagnostic roles of the studied markers in the discrimination between LUAD and LUSC and their prognostic values.

Results:

SPATS2 and CLCA2 were expressed higher in LUSC than LUAD. ST6GALNAC1 and Adipophilin showed higher expression in LUAD than LUSC (P<0.001). The sensitivity and specificity of CLCA2, SPATS2, ST6GALNAC1 and Adipophilin in adequate subtyping and reaching the accurate diagnosis was 100%. We found only significant difference in survival rate between the patients with negative and positive CLCA2 expression (P=0.038 and P=0.019, respectively).

Conclusion:

The combination of biomarkers of CLCA2, SPATS2, ST6GALNAC1, and Adipophilin may lead to an appropriate subtyping of lung cancer and reaching accurate diagnosis with the highest sensitivity and specificity.

Pathologists should probably forget about kappa. Percent agreement, diagnostic specificity and related metrics provide more clinically applicable measures of interobserver variability

Kappa statistics have been widely used in the pathology literature to compare interobserver diagnostic variability (IOV) among different pathologists but there has been limited discussion about the clinical significance of kappa scores. Five representative and recent pathology papers were queried using clinically relevant specific questions to learn how IOV was evaluated and how the clinical applicability of results was interpreted. The papers supported our anecdotal impression that pathologists usually assess IOV using Cohen's or Fleiss' kappa statistics and interpret the results using some variation of the scale proposed by Landis and Koch. The papers did not cite or propose specific guidelines to comment on the clinical applicability of results. The solutions proposed to decrease IOV included the development of better diagnostic criteria and additional educational efforts, but the possibility that the entities themselves represented a continuum of morphologic findings rather than distinct diagnostic categories was not considered in any of the studies. A dataset from a previous study of IOV reported by Thunnissen et al. was recalculated to estimate percent agreement among 19 international lung pathologists for the diagnosis of 74 challenging lung neuroendocrine neoplasms. Kappa scores and diagnostic sensitivity, specificity, positive and negative predictive values were calculated using the majority consensus diagnosis for each case as the gold reference diagnosis for that case. Diagnostic specificity estimates among multiple pathologists were > 90%, although kappa scores were considerably more variable. We explain why kappa scores are of limited clinical applicability in pathology and propose the use of positive and negative percent agreement and diagnostic specificity against a gold reference diagnosis to evaluate IOV among two and multiple raters, respectively.

A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma

The purpose of the study was to compare the texture based discriminative performances between non-contrast enhanced computed tomography (NECT) and contrast-enhanced computed tomography (CECT) images in differentiating lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) patients. Eighty-seven lung cancer subjects were enrolled in the study, including pathologically proved 47 ADC patients and 40 SCC patients, and 261 texture features were extracted from the manually delineated region of interests on CECT and NECT images respectively. Fisher score was then used to select the effective discriminative texture features between groups, and the selected texture features were adopted to differentiate ADC from SCC using Support Vector Machine and Leave-one-out cross-validation. Both NECT and CECT images could achieve the same best classification accuracy of 95.4%, and most of the informative features were from the gray-level co-occurrence matrix. In addition, CECT images were found with enhanced texture features compared with NECT images, and combining texture features of CECT and NECT images together could further improve the prediction accuracy. Besides the texture feature, the tumor location information also contributed to the differential diagnosis between ADC and SCC.