Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort.

Prostate and breast cancer incidence rates have been on the rise in Japan, emphasising the need for precise histopathological diagnosis to determine patient prognosis and guide treatment decisions. However, existing diagnostic methods face numerous challenges and are susceptible to inconsistencies between observers. To tackle these issues, artificial intelligence (AI) algorithms have been developed to aid in the diagnosis of prostate and breast cancer. This study focuses on validating the performance of two such algorithms, Galen Prostate and Galen Breast, in a Japanese cohort, with a particular focus on the grading accuracy and the ability to differentiate between invasive and non-invasive tumours. The research entailed a retrospective examination of 100 consecutive prostate and 100 consecutive breast biopsy cases obtained from a Japanese institution. Our findings demonstrated that the AI algorithms showed accurate cancer detection, with AUCs of 0.969 and 0.997 for the Galen Prostate and Galen Breast, respectively. The Galen Prostate was able to detect a higher Gleason score in four adenocarcinoma cases and detect a previously unreported cancer. The two algorithms successfully identified relevant pathological features, such as perineural invasions and lymphovascular invasions. Although further improvements are required to accurately differentiate rare cancer subtypes, these findings highlight the potential of these algorithms to enhance the precision and efficiency of prostate and breast cancer diagnosis in Japan. Furthermore, this validation paves the way for broader adoption of these algorithms as decision support tools within the Asian population.

Squamous metaplasia is an indicator of acute exacerbation in patients with usual interstitial pneumonia / idiopathic pulmonary fibrosis.

BACKGROUND: Acute exacerbation (AE) is a potentially lethal event in patients with usual interstitial pneumonia/idiopathic pulmonary fibrosis (UIP/IPF). However, to date, no pathological predictors of AE have been identified. This retrospective study aimed to elucidate the pathological features that could predict AE in patients with UIP. METHODS: We reviewed the pathological findings of 91 patients with UIP/IPF and correlated these findings with AE events. Thirteen histological variables related to acute lung injury were evaluated by three independent observers and classified as positive or negative. The patients' clinical data during follow-up were collected and reviewed for AE. A recursive partition using the Gini index for the prediction of AE was performed, with each pathological finding as a candidate for branching. RESULTS: Twenty patients (22%) developed AE during the median follow-up duration of 40 months. Thirty-eight patients died (15 due to AE and 23 for other reasons). The median time interval from surgical lung biopsy to AE onset was 497 (interquartile range: 901-1657) days. Histologically, squamous metaplasia was positively associated with AE (odds ratio: 4.7, P = 0.015) and worse event-free survival in patients with UIP (P = 0.04). Leaf scoring based on the Gini index for recursive partition, including five positive findings (squamous metaplasia, neutrophilic infiltration, septal widening, Kuhn's hyaline, and fibrin), showed a sensitivity of 90% with a specificity of 74.7% (area under curve: 0.89). CONCLUSIONS: We found that squamous metaplasia is an important histopathological finding that predicts AE events and tends to unfavorable outcome in patients with UIP/IPF.

Idiopathic dendriform pulmonary ossification as the phenotype of interstitial lung abnormalities: CT-pathologic correlation and prevalence.

BACKGROUND AND PURPOSE: Idiopathic dendriform pulmonary ossification (DPO) is mostly asymptomatic, and detected incidentally in lung CT. There have been no reports on the precise CT-pathologic correlation and the prevalence of idiopathic DPO. This study aimed to clarify the histological background and prevalence of idiopathic DPO. MATERIALS AND METHODS: Sixteen patients with histologically confirmed idiopathic DPO (12 men and 4 women; mean age, 38.8 years; range 22-56 years) were identified in a nationwide epidemiological survey. Local HRCT findings of pre-biopsy examinations, such as branching, round, linear structures with or without high attenuation were compared side by side with histological findings. The attenuation of branching, round, and linear structures was classified into three-point levels on bone window images (width, 2500 HU; level, 500 HU). Furthermore, we collected continuous pulmonary CT images of 8111 cases for checking up metastasis from extrathoracic malignancy at a single institution, and evaluated the prevalence of interstitial lung abnormalities (ILAs) and DPO. RESULTS: In all 16 cases, branching (n = 15, 93%), round (n = 5, 31%), or linear (n = 5, 31%) structures were identified, histologically corresponding to dendriform ossification and cicatricial organizing pneumonia (OP)/fibrosis. Histologically, ossification was confirmed in all the 16 patients. However, in two cases, a highly attenuated structure could not be detected on the pre-biopsy CT of the same area. Regarding the prevalence of idiopathic DPO, 283 (3.5%) of 8111 patients had ILAs, of which a total of 26 (0.3% of all cases, 9.2% of ILAs cases) had DPO. CONCLUSION: Idiopathic DPO showed linear or branching structures with or without high attenuation on CT, corresponded to ossification, cicatricial OP/fibrosis. DPO was seen in 9.2% of ILAs cases. Idiopathic DPO is one of pathologic phenotypes of ILAs.

Presence of focal usual interstitial pneumonia is a key prognostic factor in progressive pulmonary fibrosis.

AIMS: Progressive pulmonary fibrosis (PPF) is a newly recognised clinical phenotype of interstitial lung diseases in the 2022 interstitial pulmonary fibrosis (IPF) guidelines. This category is based entirely on clinical and radiological factors, and the background histopathology is unknown. Our objective was to investigate the histopathological characteristics of PPF and to examine the correlation between usual interstitial pneumonia (UIP) and prognosis in this new disease type. We hypothesised that the presence of UIP-like fibrosis predicts patients' survival in PPF cases. METHODS AND RESULTS: We selected 201 cases fulfilling the clinical criteria of PPF from case archives. Cases diagnosed as IPF by a multidisciplinary team were excluded. Whole slide images were evaluated by three pathologists who were blinded to clinical and radiological data. We measured areas of UIP-like fibrosis and calculated what percentage of the total lesion area they occupied. The presence of focal UIP-like fibrosis amounting to 10% or more of the lesion area was seen in 148 (73.6%), 168 (83.6%) and 165 (82.1%) cases for each pathologist, respectively. Agreement of the recognition of UIP-like fibrosis in PPF cases was above κ = 0.6 between all pairs. Survival analysis showed that the presence of focal UIP-like fibrosis correlated with worsened survival under all parameters tested (P < 0.001). CONCLUSIONS: The presence of UIP-like fibrosis is a core pathological feature of clinical PPF, and its presence within diseased areas is associated with poorer prognosis. This study highlights the importance of considering the presence of focal UIP-like fibrosis in the evaluation and management of PPF.

Fast-Track Development and Multi-institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Japanese guidelines for the treatment of idiopathic pulmonary fibrosis 2023:Revised edition.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a poor prognosis and an unknown cause that generally progresses to pulmonary fibrosis and leads to irreversible tissue alteration. The "Guidelines for the treatment of idiopathic pulmonary fibrosis 2017," specializing in the treatment of IPF for the first time in Japan and presenting evidence-based standard treatment methods suited to the state of affairs in Japan, was published in 2017, in line with the 2014 version of "Formulation procedure for Minds Clinical Practice Guidelines." Because new evidence had accumulated, we formulated the "Guidelines for the treatment of Idiopathic Pulmonary Fibrosis 2023 (revised 2nd edition)." While keeping the revision consistent with the ATS/ERS/JRS/ALAT IPF treatment guidelines, new clinical questions (CQs) on pulmonary hypertension were added to the chronic stage, in addition to acute exacerbation and comorbid lung cancer, which greatly affect the prognosis but are not described in the ATS/ERS/JRS/ALAT IPF guidelines. Regarding the advanced stages, we additionally created expert consensus-based advice for palliative care and lung transplantation. The number of CQs increased from 17 in the first edition to 24. It is important that these guidelines be used not only by respiratory specialists but also by general practitioners, patients, and their families; therefore, we plan to revise them appropriately in line with ever-advancing medical progress.

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.

Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer.

BACKGROUND: In small-cell lung cancer (SCLC), the tumor immune microenvironment (TIME) could be a promising biomarker for immunotherapy, but objectively evaluating TIME remains challenging. Hence, we aimed to develop a predictive biomarker of immunotherapy efficacy through a machine learning analysis of the TIME. METHODS: We conducted a biomarker analysis in a prospective study of patients with extensive-stage SCLC who received chemoimmunotherapy as the first-line treatment. We trained a model to predict 1-year progression-free survival (PFS) using pathological images (H&E, programmed cell death-ligand 1 (PD-L1), and double immunohistochemical assay (cluster of differentiation 8 (CD8) and forkhead box P3 (FoxP3)) and patient information. The primary outcome was the mean area under the curve (AUC) of machine learning models in predicting the 1-year PFS. RESULTS: We analyzed 100,544 patches of pathological images from 78 patients. The mean AUC values of patient information, pathological image, and combined models were 0.789 (range 0.571-0.982), 0.782 (range 0.750-0.911), and 0.868 (range 0.786-0.929), respectively. The PFS was longer in the high efficacy group than in the low efficacy group in all three models (patient information model, HR 0.468, 95% CI 0.287 to 0.762; pathological image model, HR 0.334, 95% CI 0.117 to 0.628; combined model, HR 0.353, 95% CI 0.195 to 0.637). The machine learning analysis of the TIME had better accuracy than the human count evaluations (AUC of human count, CD8-positive lymphocyte: 0.681, FoxP3-positive lymphocytes: 0.626, PD-L1 score: 0.567). CONCLUSIONS: The spatial analysis of the TIME using machine learning predicted the immunotherapy efficacy in patients with SCLC, thus supporting its role as an immunotherapy biomarker.

Japanese clinical practice guide 2022 for hypersensitivity pneumonitis.

Considering recently published two guidelines for the diagnosis of hypersensitivity pneumonitis (HP), the Japanese Respiratory Society (JRS) has now published its own Japanese clinical practice guide for HP. Major types of HP in Japan include summer-type, home-related, bird-related, farmer's lung, painter's lung, humidifier lung, and mushroom grower's lung. Identifying causative antigens is critical for increasing diagnostic confidence, as well as improving prognosis through appropriate antigen avoidance. This guide proposes a comprehensive antigen questionnaire including the outbreak sources reported in Japan. Drawing on the 2021 CHEST guideline, this guide highlights the antigen identification confidence level and adaptations for environmental surveys. The detection of specific antibodies against causative antigens is an important diagnostic predictor of HP. In Japan, the assessments of bird-specific IgG (pigeons, budgerigars) and the Trichosporon asahii antibody are covered by medical insurance. Although this guide adopts the 2020 ATS/JRS/ALAT guideline diagnostic criteria based on the combination of imaging findings, exposure assessment, bronchoalveolar lavage lymphocytosis, and histopathological findings, it added some annotations to facilitate the interpretation of the content and correlate the medical situation in Japan. It recommends checking biomarkers; seasonal changes in the KL-6 concentration (increase in winter for bird-related HP/humidifier lung and in summer for summer-type HP) and high KL-6 concentrations providing a basis for the suspicion of HP. Antigen avoidance is critical for disease management of HP. This guide also addresses the pharmacological management of HP, highlighting the treatment strategy for fibrotic HP including combination therapies with anti-inflammatory/immunosuppressive and antifibrotic drugs.

Left Main Bronchus Obstruction in a Patient with Small-cell Lung Cancer Successfully Treated with Venovenous Extracorporeal Membrane Oxygenation.

Lung cancer can cause fatal central airway obstruction. Rapid airway clearance is necessary in some cases, but ventilator management may be insufficient to maintain oxygenation levels. Venovenous extracorporeal membrane oxygenation (VV-ECMO) may be an effective rescue therapy for respiratory failure, but its efficacy in treating tumor-related airway obstruction is unknown. We herein report a case of central airway obstruction and severe acute respiratory failure due to small-cell lung cancer successfully treated with VV-ECMO, bronchoscopic airway intervention, and chemotherapy. VV-ECMO can be an effective option for the treatment of central airway obstruction with acute respiratory failure due to lung cancer.

Stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities on CT.

High-attenuation pulmonary abnormalities are commonly seen on CT. These findings are increasingly encountered with the growing number of CT examinations and the wide availability of thin-slice images. The abnormalities include benign lesions, such as infectious granulomatous diseases and metabolic diseases, and malignant tumors, such as lung cancers and metastatic tumors. Due to the wide spectrum of diseases, the proper diagnosis of high-attenuation abnormalities can be challenging. The assessment of these abnormal findings requires scrutiny, and the treatment is imperative. Our proposed stepwise diagnostic algorithm consists of five steps. Step 1: Establish the presence or absence of metallic artifacts. Step 2: Identify associated nodular or mass-like soft tissue components. Step 3: Establish the presence of solitary or multiple lesions if identified in Step 2. Step 4: Ascertain the predominant distribution in the upper or lower lungs if not identified in Step 2. Step 5: Identify the morphological pattern, such as linear, consolidation, nodular, or micronodular if not identified in Step 4. These five steps to diagnosing high-attenuation abnormalities subdivide the lesions into nine categories. This stepwise radiologic diagnostic approach could help to narrow the differential diagnosis for various pulmonary high-attenuation abnormalities and to achieve a precise diagnosis.Critical relevance statement Our proposed stepwise diagnostic algorithm for high-attenuation pulmonary abnormalities may help to recognize a variety of those high-attenuation findings, to determine whether the associated diseases require further investigation, and to guide appropriate patient management. Key points • To provide a stepwise diagnostic approach to high-attenuation pulmonary abnormalities.• To familiarize radiologists with the varying cause of high-attenuation pulmonary abnormalities.• To recognize which high-attenuation abnormalities require scrutiny and prompt treatment.

Real-World Implementation of Digital Pathology: Results From an Intercontinental Survey.

The past 70 years have been characterized by rapid advancements in computer technology, and the health care system has not been immune to this trend. However, anatomical pathology has remained largely an analog discipline. In recent years, this has been changing with the growing adoption of digital pathology, partly driven by the potential of computer-aided diagnosis. As part of an international collaboration, we conducted a comprehensive survey to gain a deeper understanding of the status of digital pathology implementation in Europe and Asia. A total of 127 anatomical pathology laboratories participated in the survey, including 75 from Europe and 52 from Asia, with 72 laboratories having established digital pathology workflow and 55 without digital pathology. Laboratories using digital pathology for diagnostic (n = 29) and nondiagnostic (n = 43) purposes were thoroughly questioned about their implementation strategies and institutional experiences, including details on equipment, storage, integration with laboratory information system, computer-aided diagnosis, and the costs of going digital. The impact of the digital pathology workflow was also evaluated, focusing on turnaround time, specimen traceability, quality control, and overall satisfaction. Laboratories without access to digital pathology were asked to provide insights into their perceptions of the technology, expectations, barriers to adoption, and potential facilitators. Our findings indicate that although digital pathology is still the future for many, it is already the present for some. This decade may be a time when anatomical pathology finally embraces digital revolution on a larger scale.

Pulmonary Langerhans cell histiocytosis diagnosed using transbronchial lung cryobiopsy: A case report.

A 63-year-old Japanese woman with multiple cysts in both lungs on chest computed tomography (CT) was referred to our hospital after a thorough examination, including a transbronchial lung biopsy (TBLB), failed to provide a diagnosis. Based on the findings on chest CT and pathological examination of the bronchoalveolar lavage fluid and transbronchial lung cryobiopsy (TBLC) specimen, the patient was diagnosed with pulmonary Langerhans cell histiocytosis (PLCH). TBLC may replace TBLB as the main diagnostic technique for PLCH, although further studies are required to determine the usefulness of TBLC for the diagnosis of PLCH.

Frequency of subclinical interstitial lung disease in COVID-19 autopsy cases: potential risk factors of severe pneumonia.

Risk factors of severe coronavirus disease 2019 (COVID-19) have been previously reported; however, histological risk factors have not been defined thus far. The aim of this study was to clarify subclinical hidden interstitial lung disease (ILD) as a risk factor of severe pneumonia associated with COVID-19. We carefully examined autopsied lungs and chest computed tomography scanning (CT) images from patients with COVID-19 for interstitial lesions and then analyzed their relationship with disease severity. Among the autopsy series, subclinical ILD was found in 13/27 cases (48%) in the COVID-19 group, and in contrast, 8/65 (12%) in the control autopsy group (p = 0.0006; Fisher's exact test). We reviewed CT images from the COVID-19 autopsy cases and verified that subclinical ILD was histologically detectable in the CT images. Then, we retrospectively examined CT images from another series of COVID-19 cases in the Yokohama, Japan area between February-August 2020 for interstitial lesions and analyzed the relationship to the severity of COVID-19 pneumonia. Interstitial lesion was more frequently found in the group with the moderate II/severe disease than in the moderate I/mild disease (severity was evaluated according to the COVID-19 severity classification system of the Ministry of Health, Labor, and Welfare [Japan]) (moderate II/severe, 11/15, 73.3% versus moderate I/mild, 108/245, 44.1%; Fisher exact test, p = 0.0333). In conclusion, it was suggested that subclinical ILD could be an important risk factor for severe COVID-19 pneumonia. A benefit of these findings could be the development of a risk assessment system using high resolution CT images for fatal COVID-19 pneumonia.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma.

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning.

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 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 convolutional neural networks 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.

Changes in patient-reported outcomes in patients with non-idiopathic pulmonary fibrosis fibrotic interstitial lung disease and progressive pulmonary fibrosis.

Background: Health-related quality of life (HRQoL) captures different aspects of the fibrotic interstitial lung disease (FILD) evaluation from the patient's perspective. However, little is known about how HRQoL changes in patients with non-idiopathic pulmonary fibrosis (IPF) FILD, especially in those with progressive pulmonary fibrosis (PPF). The aim of this study is to clarify whether HRQoL deteriorates in patients with non-IPF FILD and to evaluate the differences in the changes in HRQoL between those with and without PPF. Methods: We collected data from consecutive patients with non-IPF FILD and compared annual changes in HRQoL over 2 years between patients with PPF and those without. The St George's respiratory questionnaire (SGRQ) and COPD assessment test (CAT) were used to assess HRQoL. Changes in the SGRQ and CAT scores for 24 months from baseline were evaluated with a mixed-effect model for repeated measures. Results: A total of 396 patients with non-IPF FILD were reviewed. The median age was 65 years and 202 were male (51.0%). The median SGRQ and CAT scores were 29.6 and 11, respectively. Eighty-six (21.7%) showed PPF. Both SGRQ and CAT scores were significantly deteriorated in patients with PPF compared to those without PPF (p < 0.01 for both). Clinically important deterioration in the SGRQ and CAT scores were observed in 40.0 and 35.7% of patients with PPF and 11.7 and 16.7% of those without, respectively. PPF was significantly associated with clinically important deterioration in the SGRQ score (odds ratio 5.04; 95%CI, 2.61-9.76, p < 0.01) and CAT score (odds ratio 2.78; 95%CI, 1.27-6.06, p = 0.02). Conclusion: The SGRQ and CAT scores were significantly deteriorated in patients with non-IPF FILD and PPF. Considering an evaluation of HRQoL would be needed when assessing PPF.