AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma.

OBJECTIVES: Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules. MATERIALS AND METHODS: Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments. RESULTS: Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies. CONCLUSION: In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.

Involvement of 27-hydroxycholesterol on the progression of non-small cell lung cancer via the estrogen receptor.

The oxysterol 27-hydroxycholesterol (27HC) promotes the proliferation of breast cancer cells as a selective estrogen receptor modulator (SERM), but it is mostly produced by alveolar macrophages in vivo. The present study evaluated hypothesis that 27HC may also promote the proliferation of lung cancer cells. In the tumor and nontumor regions of lung tissue from 23 patients with non-small cell lung cancer (NSCLC) who underwent lung cancer surgery, we compared the 27HC content and its synthetic and catabolic enzyme expressions (CYP27A1 and CYP7B1), the expressions of the estrogen receptor (ER) gene and its target gene cMYC by using high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS), real-time RT-PCR, and immunohistochemical staining. In addition, we evaluated the effects of 27HC and ß-estradiol (E2) treatments on the proliferation of a cultured lung cancer cell line (H23 cells) expressing ERß. In squamous cell carcinoma and in adenocarcinoma, the 27HC content was significantly higher in the tumor region than in the nontumor region, and in cancer grade III than in the other cancer grades. CYP27A1-positive macrophages were histologically detected in the nontumor regions of both cancer types, whereas the gene and protein expressions of ERß, as well as the CYP7B1 and cMYC genes, were significantly increased in the tumor tissues. In cultured H23 cells, proliferation was significantly increased by 27HC and E2 treatments for 48 h. Similar to breast cancer, the present results supported idea that the 27HC produced from alveolar macrophages promotes the proliferation of lung cancer cells highly expressing ER through the SERM action. Therefore, 27HC should be an important target for cancer therapy of NSCLC.

Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation.

PURPOSE: Histopathological imaging is widely used for the analysis and diagnosis of multiple diseases. Several methods have been proposed for the 3D reconstruction of pathological images, captured from thin sections of a given specimen, which get nonlinearly deformed due to the preparation process. The majority of the available methods for registering such images use the degree of matching of adjacent images as the criteria for registration, which can result in unnatural deformations of the anatomical structures. Moreover, most methods assume that the same staining is used for all images, when in fact multiple staining is usually applied in order to enhance different structures in the images. METHODS: This paper proposes a non-rigid 3D reconstruction method based on the assumption that internal structures on the original tissue must be smooth and continuous. Landmarks are detected along anatomical structures using template matching based on normalized cross-correlation (NCC), forming jagged shape trajectories that traverse several slices. The registration process smooths out these trajectories and deforms the images accordingly. Artifacts are automatically handled by using the confidence of the NCC in order to reject unreliable landmarks. RESULTS: The proposed method was applied to a large series of histological sections from the pancreas of a KPC mouse. Some portions were dyed primarily with HE stain, while others were dyed alternately with HE, CK19, MT and Ki67 stains. A new evaluation method is proposed to quantitatively evaluate the smoothness and isotropy of the obtained reconstructions, both for single and multiple staining. CONCLUSIONS: The experimental results show that the proposed method produces smooth and nearly isotropic 3D reconstructions of pathological images with either single or multiple stains. From these reconstructions, microanatomical structures enhanced by different stains can be simultaneously observed.