Difficulties in diagnostics of lung tumours in biopsies: an interpathologist concordance study evaluating the international diagnostic guidelines

Navigating the Maze: Exploring Non-Oncological Complexities in Non-Small-Cell Lung Cancer

Pulmonary oncological pathologies are an important public health problem and the association with other pulmonary lesions may pose difficulties in diagnosis and staging or require different treatment options. To address this complexity, we conducted a retrospective observational study at the Marius Nasta Institute of Pneumophthisiology, Bucharest, Romania. Our study focused on patients admitted in 2019 with non-small-cell lung carcinoma and associated pulmonary lesions identified through surgical resection specimens. Among the 314 included patients, multiple pulmonary nodules were observed on macroscopic examination, with 12% (N = 37) exhibiting nonmalignant etiologies upon microscopic examination. These findings underscore the challenge of preoperative staging. Patients with coexisting nonmalignant lesions were similar in age, smoking habits, and professional or environmental exposure by comparison with those who presented only malignant lesions. The presentation of coexisting malignant and nonmalignant lesions may pose difficulties in diagnosing and staging pulmonary cancer.

The Complementary Role of PET to Pathology in Differentiating the Primary Origin of a Malignant Skin Nodule from Liver or Lung

Metastasis from unknown primary is always a challenge because finding the true primary tumor significantly affects subsequent management. We present a case of malignant abdominal wall nodule initially diagnosed as metastasis from hepatocellular carcinoma through excisional biopsy and immunohistochemical (IHC) staining. Dual-tracer positron emission tomography/computed tomography (PET/CT) with 11C-acetate and 18F-FDG, however, showed metabolic findings in favor of metastasis from lung origin, which was finally confirmed by ensuing a lung biopsy with additional IHC stains. This case illustrates the complementary molecular role of PET to pathology, particularly when dual-tracer or multi-tracer PET is used in conjunction with pathology methods for cross referencing and confirmation.

Radiomics in Lung Metastases: A Systematic Review

Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.

Automatic segmentation of breast cancer histological images based on dual-path feature extraction network

The traditional manual breast cancer diagnosis method of pathological images is time-consuming and labor-intensive, and it is easy to be misdiagnosed. Computer-aided diagnosis of WSIs gradually comes into people*s sight. However, the complexity of high-resolution breast cancer pathological images poses a great challenge to automatic diagnosis, and the existing algorithms are often difficult to balance the accuracy and efficiency. In order to solve these problems, this paper proposes an automatic image segmentation method based on dual-path feature extraction network for breast pathological WSIs, which has a good segmentation accuracy. Specifically, inspired by the concept of receptive fields in the human visual system, dilated convolutional networks are introduced to encode rich contextual information. Based on the channel attention mechanism, a feature attention module and a feature fusion module are proposed to effectively filter and combine the features. In addition, this method uses a light-weight backbone network and performs pre-processing on the data, which greatly reduces the computational complexity of the algorithm. Compared with the classic models, it has improved accuracy and efficiency and is highly competitive.

Clinical validation of a next-generation sequencing-based multi-cancer early detection "liquid biopsy" blood test in over 1,000 dogs using an independent testing set: The CANcer Detection in Dogs (CANDiD) study

Cancer is the leading cause of death in dogs, yet there are no established screening paradigms for early detection. Liquid biopsy methods that interrogate cancer-derived genomic alterations in cell-free DNA in blood are being adopted for multi-cancer early detection in human medicine and are now available for veterinary use. The CANcer Detection in Dogs (CANDiD) study is an international, multi-center clinical study designed to validate the performance of a novel multi-cancer early detection “liquid biopsy” test developed for noninvasive detection and characterization of cancer in dogs using next-generation sequencing (NGS) of blood-derived DNA; study results are reported here. In total, 1,358 cancer-diagnosed and presumably cancer-free dogs were enrolled in the study, representing the range of breeds, weights, ages, and cancer types seen in routine clinical practice; 1,100 subjects met inclusion criteria for analysis and were used in the validation of the test. Overall, the liquid biopsy test demonstrated a 54.7% (95% CI: 49.3–60.0%) sensitivity and a 98.5% (95% CI: 97.0–99.3%) specificity. For three of the most aggressive canine cancers (lymphoma, hemangiosarcoma, osteosarcoma), the detection rate was 85.4% (95% CI: 78.4–90.9%); and for eight of the most common canine cancers (lymphoma, hemangiosarcoma, osteosarcoma, soft tissue sarcoma, mast cell tumor, mammary gland carcinoma, anal sac adenocarcinoma, malignant melanoma), the detection rate was 61.9% (95% CI: 55.3–68.1%). The test detected cancer signal in patients representing 30 distinct cancer types and provided a Cancer Signal Origin prediction for a subset of patients with hematological malignancies. Furthermore, the test accurately detected cancer signal in four presumably cancer-free subjects before the onset of clinical signs, further supporting the utility of liquid biopsy as an early detection test. Taken together, these findings demonstrate that NGS-based liquid biopsy can offer a novel option for noninvasive multi-cancer detection in dogs.

Real-World Diagnostic Accuracy and Use of Immunohistochemical Markers in Lung Cancer Diagnostics

OBJECTIVES

Accurate and reliable diagnostics are crucial as histopathological type influences selection of treatment in lung cancer. The aim of this study was to evaluate real-world accuracy and use of immunohistochemical (IHC) staining in lung cancer diagnostics.

MATERIALS AND METHODS

The diagnosis and used IHC stains for small specimens with lung cancer on follow-up resection were retrospectively investigated for a 15-month period at two major sites in Sweden. Additionally, 10 pathologists individually suggested diagnostic IHC staining for 15 scanned bronchial and lung biopsies and cytological specimens.

RESULTS

In 16 (4.7%) of 338 lung cancer cases, a discordant diagnosis of potential clinical relevance was seen between a small specimen and the follow-up resection. In half of the cases, there was a different small specimen from the same investigational work-up with a concordant diagnosis. Diagnostic inaccuracy was often related to a squamous marker not included in the IHC panel (also seen for the scanned cases), the case being a neuroendocrine tumor, thyroid transcription factor-1 (TTF-1) expression in squamous cell carcinomas (with clone SPT24), or poor differentiation. IHC was used in about 95% of cases, with a higher number of stains in biopsies and in squamous cell carcinomas and especially neuroendocrine tumors. Pre-surgical transthoracic samples were more often diagnostic than bronchoscopic ones (72-85% vs. 9-53% for prevalent types).

CONCLUSIONS

Although a high overall diagnostic accuracy of small specimens was seen, small changes in routine practice (such as consequent inclusion of p40 and TTF-1 clone 8G7G3/1 in the IHC panel for non-small cell cancer with unclear morphology) may lead to improvement, while reducing the number of IHC stains would be preferable from a time and cost perspective.

Clinical-Pathologic Challenges in the Classification of Pulmonary Neuroendocrine Neoplasms and Targets on the Horizon for Future Clinical Practice

Diagnosing a pulmonary neuroendocrine neoplasm (NEN) may be difficult, challenging clinical decision making. In this review, the following key clinical and pathologic issues and informative molecular markers are being discussed: (1) What is the preferred outcome parameter for curatively resected low-grade NENs (carcinoid), for example, overall survival or recurrence-free interval? (2) Does the WHO classification combined with a Ki-67 proliferation index and molecular markers, such as OTP and CD44, offer improved prognostication in low-grade NENs? (3) What is the value of a typical versus atypical carcinoid diagnosis on a biopsy specimen in local and metastatic disease? Diagnosis is difficult in biopsy specimens and recent observations of an increased mitotic rate in metastatic carcinoid from typical to atypical and high-grade NEN can further complicate diagnosis. (4) What is the (ir)relevance of morphologically separating large cell neuroendocrine carcinoma (LCNEC) SCLC and the value of molecular markers (RB1 gene and pRb protein or transcription factors NEUROD1, ASCL1, POU2F3, or YAP1 [NAPY]) to predict systemic treatment outcome? (5) Are additional diagnostic criteria required to accurately separate LCNEC from NSCLC in biopsy specimens? Neuroendocrine morphology can be absent owing to limited sample size leading to missed LCNEC diagnoses. Evaluation of genomic studies on LCNEC and marker studies have identified that a combination of napsin A and neuroendocrine markers could be helpful. Hence, to improve clinical practice, we should consider to adjust our NEN classification incorporating prognostic and predictive markers applicable on biopsy specimens to inform a treatment outcome-driven classification.