Fukuoka, Junya

ABSTRACTBackground and ObjectiveThe diagnosis of interstitial lung diseases (ILDs) often relies on the integration of various clinical, radiological, and histopathological data. Achieving high diagnostic accuracy in ILDs, particularly for distinguishing usual interstitial pneumonia (UIP), is challenging and requires a multidisciplinary approach. Therefore, this study aimed to develop a multimodal artificial intelligence (AI) algorithm that combines computed tomography (CT) and histopathological images to improve the accuracy and consistency of UIP diagnosis.MethodsA dataset of CT and pathological images from 324 patients with ILD between 2009 and 2021 was collected. The CT component of the model was trained to identify 28 different radiological features. The pathological counterpart was developed in our previous study. A total of 114 samples were selected and used for testing the multimodal AI model. The performance of the multimodal AI was assessed through comparisons with expert pathologists and general pathologists.ResultsThe developed multimodal AI demonstrated a substantial improvement in distinguishing UIP from non‐UIP, achieving an AUC of 0.92. When applied by general pathologists, the diagnostic agreement rate improved significantly, with a post‐model κ score of 0.737 compared to 0.273 pre‐model integration. Additionally, the diagnostic consensus rate with expert pulmonary pathologists increased from κ scores of 0.278–0.53 to 0.474–0.602 post‐model integration. The model also increased diagnostic confidence among general pathologists.ConclusionCombining CT and histopathological images, the multimodal AI algorithm enhances pathologists' diagnostic accuracy, consistency, and confidence in identifying UIP, even in cases where specialised expertise is limited. image

Context.— The accurate identification of different lung adenocarcinoma histologic subtypes is important for determining prognosis but can be challenging because of overlaps in the diagnostic features, leading to considerable interobserver variability. Objective.— To provide an overview of the diagnostic agreement for lung adenocarcinoma subtypes among pathologists and to create a ground truth using the clustering approach for downstream computational applications. Design.— Three sets of lung adenocarcinoma histologic images with different evaluation levels (small patches, areas with relatively uniform histology, and whole slide images) were reviewed by 17 international expert lung pathologists and 1 pathologist in training. Each image was classified into one or several lung adenocarcinoma subtypes. Results.— Among the 4702 patches of the first set, 1742 (37%) had an overall consensus among all pathologists. The overall Fleiss κ score for the agreement of all subtypes was 0.58. Using cluster analysis, pathologists were hierarchically grouped into 2 clusters, with κ scores of 0.588 and 0.563 in clusters 1 and 2, respectively. Similar results were obtained for the second and third sets, with fair-to-moderate agreements. Patches from the first 2 sets that obtained the consensus of the 18 pathologists were retrieved to form consensus patches and were regarded as the ground truth of lung adenocarcinoma subtypes. Conclusions.— Our observations highlight discrepancies among experts when assessing lung adenocarcinoma subtypes. However, a subsequent number of consensus patches could be retrieved from each cluster, which can be used as ground truth for the downstream computational pathology applications, with minimal influence from interobserver variability.

Abstract Objectives Numerous studies on malignant mesothelioma (MM) highlight the prognostic importance of histologic subtype, nuclear grade, and necrosis. This study compares these parameters in paired biopsy and resection specimens of pleural MM. Methods Histologic subtype, percentage of epithelioid morphology, nuclear grade, and the presence or absence of necrosis were compared in 429 paired biopsies and resection specimens of pleural MM from 19 institutions. Results Histologic subtype was concordant in 81% of cases (κ = 0.58). When compared with resection specimens, epithelioid morphology at biopsy had a positive predictive value (PPV) of 78.9% and a negative predictive value (NPV) of 93.5%; sarcomatoid morphology showed high PPV (92.9%) and NPV (99.3%), and biphasic morphology PPV was 89.7% and NPV was 79.7%. Agreement of the percentage of epithelioid morphology was fair (κ = 0.27). Nuclear grade and necrosis were concordant in 75% (κ = 0.59) and 81% (κ = 0.53) of cases, respectively. Nuclear grade showed moderate (κ = 0.53) and substantial (κ = 0.67) agreement from patients with and without neoadjuvant therapy, respectively, and necrosis showed moderate (κ = 0.47 and κ = 0.60) agreement, respectively, in the same subsets of paired specimens. Conclusions Paired biopsy-resection specimens from pleural MM show overall moderate agreement in pathologic parameters. These findings may help guide postbiopsy management and triage of patients with MM.

Abstract Purpose: The eukaryotic translation initiation factor complex 4E (eIF4E) is downstream in the mammalian target of rapamycin (mTOR) pathway. This study explored expression of eIF4E and its relationship with the PTEN/AKT and RAS/MEK/ERK pathways in non–small cell lung carcinoma (NSCLC). Experimental Design: The status of phosphorylated eIF4E (p-eIF4E), phosphorylated AKT (p-AKT), PTEN, phosphorylated tuberin (p-TSC2), phosphorylated mTOR (p-mTOR), phosphorylated S6 (p-S6), and phosphorylated Erk1/2 (p-Erk1/2) was studied using immunohistochemical analysis applied to a tissue microarray containing 300 NSCLCs. Staining results for each antibody were compared with clinical and pathologic features, and the relationship between staining results was explored. Results: Overexpression of p-eIF4E, p-AKT, p-TSC2, p-mTOR, p-S6, and p-Erk1/2 in NSCLC was found in 39.9%, 78.8%, 5.1%, 46.7%, 27.1%, and 16.6% of tumors, respectively. The phenotype of p-eIF4E correlated positively with that of p-AKT, p-TSC2, and p-S6 (P < 0.001). Overall survival in NSCLC patients was significantly shorter in cases with overexpression of p-eIF4E and p-AKT alone and in combination (log-rank P < 0.001, each). Cases with underexpression of PTEN were limited (6.4%), and this phenotype did not correlate with any clinical variable. In cluster analysis, the p-AKT/p-mTOR/p-eIF4E/p-S6–positive group had significantly shorter survival compared with the survival of all cases (P < 0.001). Multivariate analysis showed that p-eIF4E overexpression is an independent prognostic factor for NSCLC (P = 0.004). Conclusions: This study shows that p-eIF4E expression in addition to p-AKT predicts poor prognosis in NSCLC. Moreover, the correlation between expression of p-eIF4E with p-AKT, as well as p-TSC2 and p-S6, indicates that eIF4E activation through the AKT pathway plays an important role in the progression of NSCLC. Clin Cancer Res; 16(1); OF1–9

Abstract We evaluated somatic genetic alterations in the kinase domain of the EGFR gene in the tumors of 219 non–small cell lung cancer patients of primarily Caucasian and African American origins. We identified 26 patients (12%) whose tumors had a mutation in the EGFR gene, and 11 (5%) patients carried novel genomic variations consistent with germ-line polymorphisms. All but one mutation were identified in Caucasian patients affected with adenocarcinoma. EGFR mutations were more frequent in women and in nonsmokers, but a significant portion of the affected patients were men (12 of 26) and current or past smokers accounted for half of the patients affected (13 of 26). Screening subjects with EGFR mutations may identify patients whose tumors could respond to targeted therapy using tyrosine kinase inhibitors.