Pembrolizumab-induced pneumonitis with a perilymphatic nodular pattern in a lung cancer patient: A radio-pathologic correlation

A 66-year-old Japanese man with recurrent adenocarcinoma of the lung p-stage IIIA (pT2bN2M0; version 8) on pembrolizumab was present with gradually worsening dyspnea. Although history and physical examination were unremarkable, high-resolution CT showed the perilymphatic distribution of the pembrolizumab-induced pneumonitis. Consistent with the CT result, biopsy revealed the aggregation of the cytotoxic (CD8+) T-lymphocytes around the lymph tracts. Given the clinical, radiological and pathological findings, pembrolizumab-induced pneumonitis was confirmed. The patient was discharged after terminating the pembrolizumab with ameliorated symptoms. This report, in conjunction with existing literature, illustrates the wide variety of the pembrolizumab-induced pneumonitis and bolsters the current understanding of its pathophysiology. Keywords: Pembrolizumab, Lung cancer, irA

Two Cases of Severe Ulcerative Colitis with Colonic Dilatation Resolved with Tacrolimus Therapy

We report 2 cases of ulcerative colitis (UC) with intestinal tract dilatation treated with tacrolimus. They were 53- and 64-year-old males, who had been admitted to local hospitals for increasing severity of their UC symptoms. Treatment for severe UC was immediately started, but both cases were refractory to corticosteroid therapy; they were then transferred to our hospital. When they were referred to our hospital, they had frequent bloody diarrhea, fever, severe abdominal pain, and even dilatation of the transverse colon on abdominal X-ray test. They were treated with oral tacrolimus medication, and their symptoms improved immediately. Dilatation of the transverse colon was improved on plain X-ray at 2 weeks after starting therapy, and emergency colectomy could be avoided. These 2 cases may suggest that tacrolimus is effective for UC with colonic dilatation as a rescue therapy

Effect of the 2020 hypersensitivity pneumonitis guideline on the pathologic diagnosis of interstitial pneumonia

Abstract It was reported that the 2020 guideline for hypersensitivity pneumonitis (HP) might result in the overdiagnosis of fibrotic HP (fHP). fHP and other types of interstitial pneumonias have several overlapping characteristics, and a high diagnostic concordance rate of fHP is rarely obtained. Therefore, we investigated the impact of the 2020 HP guideline on the pathological diagnosis of cases previously diagnosed as interstitial pneumonia. We identified 289 fibrotic interstitial pneumonia cases from 2014 to 2019 and classified them into four categories according to the 2020 HP guideline: typical, probable, and indeterminate for fHP and alternative diagnosis. The original pathological diagnosis of 217 cases were compared to their classification as either typical, probable, or indeterminate for fHP according to the 2020 guideline. The clinical data, including serum data and pulmonary function tests, were compared among the groups. Diagnoses changed from non-fHP to fHP for 54 (25%) of the 217 cases, of which, 8 were typical fHP and 46 were probable fHP. The ratio of typical and probable fHP cases to the total number of VATS cases was significantly lower when using transbronchial lung cryobiopsy (p < 0.001). The clinical data of these cases bore a more remarkable resemblance to those diagnosed as indeterminate for fHP than to those diagnosed as typical or probable. The pathological criteria in the new HP guidelines increase the diagnosis of fHP. However, it is unclear whether this increase leads to overdiagnosis, and requires further investigation. Transbronchial lung cryobiopsy may not be helpful when using the new criteria to impart findings for fHP diagnosis

Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy

Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. Methods: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Results: Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set. Conclusion: The machine-learning model developed has the potential to distinguish between benign and malignant conditions in TBLB samples excluding the presence or absence of tumor cells, thereby improving diagnostic accuracy and reducing the burden of repeated sampling procedures for patients

Standardized classification of lung adenocarcinoma subtypes and improvement of grading assessment through deep learning

The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1-scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained CNNs improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice