Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3–523) days, longer than that for radiologists (8 (0–263) days). The AI model can assist radiologists in the early detection of lung nodules.

Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4–5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.

The aim of this study was to investigate the clinical relevance of thoracic high-resolution computed tomography (HRCT) in evaluating the severity and outcome of Pneumocystis jirovecii pneumonia (PJP) in non-AIDS immunocompromised patients.We measured mean lung attenuation (MLA) and extent of increased attenuation (EIA) of PJP lesions on thoracic HRCT in 40 non-AIDS immunocompromised patients with PJP diagnosed by demonstration of the pathogens in cytological smears of bronchoalveolar lavage fluid. The MLA and EIA of PJP lesions on thoracic HRCT were used to investigate the severity of PJP. Clinically, the severity of PJP was determined by arterial oxygen tension/fraction of inspired oxygen concentration (PaO2/FiO2) ratio, acute physiology and chronic health evaluation (APACHE) II scores, the need of mechanical ventilation, and death.MLA highly correlated with EIA of PJP lesions (ρ = 0.906, P < 0.001). MLA and EIA of PJP lesions significantly correlated with PaO2/FiO2 (ρ = -0.481 and -0.370, respectively and P = 0.007 and 0.044, respectively). When intensive care unit (ICU) admission and HRCT performed were within 2 days, MLA and EIA of PJP lesions were significantly correlated with APACHE II score (ρ = 0.791 and 0.670, respectively and P = 0.001 and 0.009, respectively). There were significant differences in the values of MLA and EIA of PJP lesions between patients with and without assisted mechanical ventilator (MLA, median and [interquartile range, IQR, 25%, 75%] -516.44 [-572.10, -375.34] vs -649.27 [-715.62, -594.01], P < 0.001 and EIA, median and [IQR 25%, 75%] 0.75 [0.66, 0.82] vs 0.53 [0.45, 0.68], P = 0.003, respectively). The data of MLA and EIA of PJP lesions had limited value in identifying survivors and non-survivors.The MLA and EIA values of PJP lesions measured on thoracic HRCT might be valuable in assessing the severity of PJP in non-AIDS immunocompromised patients, but might have limited value in predicting the mortality of the patients.

Introduction: Even though alectinib is a potent second-generation ALK inhibitor with a favorable safety profile, alectinib-induced interstitial lung disease (ILD) could be fatal. There are case reports described successful alectinib rechallenge in mild ILD. However, the feasibility and safety of rechallenge in severe cases remains to be elucidated. Case report: A 76-year-old female was a case of stage IV lung adenocarcinoma harboring ALK rearrangement. Respiratory failure following severe ILD developed one month after alectinib administration. She received mechanical ventilation in intensive care uint. ILD subsided gradually after methylprednisolone pulse therapy and discontinuation of alectinib. Management and outcome: After the recovery from ILD, the patient attempted a re-escalation of alectinib from a lower dose under close clinical and radiological monitoring. No ILD happened even after 480 days of alectinib rechallenge. Discussion: Given that the ALK inhibitors are the treatment of choice for advanced lung cancer patients with ALK rearrangement. Our report demonstrated the potential feasibility of alectinib re-use in cases of severe druginduced ILD.

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.