Pulmonary Langerhans cell histiocytosis (PLCH) is a rare, multi-systemic disease primarily affecting young male adults with a history of smoking. The two patients with PLCH in our report showed relatively early and atypical radiologic presentations at initial evaluation. On chest CT, PLCH presents variable radiologic features depending on the evolutional stage of the disease. Atypical CT features of PLCH may render precise radiologic diagnosis difficult and usually require lung biopsy for a confirmation of the diagnosis. Our case review is aimed at raising the awareness of radiologists on the atypical CT features of PLCH, to help make accurate radiologic diagnosis and prevent unnecessary and invasive diagnostic procedures.

Pulmonary Langerhans cell histiocytosis (PLCH) is a rare, multi-systemic disease primarily affecting young male adults with a history of smoking. The two patients with PLCH in our report showed relatively early and atypical radiologic presentations at initial evaluation. On chest CT, PLCH presents variable radiologic features depending on the evolutional stage of the disease. Atypical CT features of PLCH may render precise radiologic diagnosis difficult and usually require lung biopsy for a confirmation of the diagnosis. Our case review is aimed at raising the awareness of radiologists on the atypical CT features of PLCH, to help make accurate radiologic diagnosis and prevent unnecessary and invasive diagnostic procedures.

BACKGROUND: Many terminally ill patients die while receiving life-sustaining treatment. Recently, the discussion of life-sustaining treatment in intensive care units (ICUs) has increased. This study is aimed to evaluate the current status of medical decision-making for dying patients. METHODS: The medical records of patients who had died in the medical ICU from March 2011 to February 2012 were reviewed retrospectively. RESULTS: Eighty-nine patients were enrolled. Their mean age was 65.8 +/- 13.3 years and 73.0% were male. The most common diagnosis was acute respiratory failure, and the most common comorbidity was hemato-oncologic malignancy. Withdrawing or withholding life-sustaining treatment including do-not-resuscitate (DNR) orders was discussed for 64 (71.9%) patients. In almost all cases, the discussion involved a physician and the patient's family. No patient wrote advance directives themselves before ICU admission. Of the patients for whom withdrawing or withholding life-sustaining treatment was discussed, the decisions were recorded in formal consent documents in 36 (56.3%) cases, while 28 (43.7%) cases involved verbal consent. In patients granting verbal consent, death within one day of the consent was more common than in those with formal document consent (85.7% vs. 61.1%, p < 0.05). The most common demand was a DNR order. Patients died 2.7 +/- 1.0 days after the decision for removal of life-sustaining treatment. CONCLUSIONS: The decision-making for life-sustaining treatment of dying patients in the ICU very often involves conflict. There is a general need to heighten our sensitivity on the objective decision-making based on patient autonomy.
Advance Directives ; Comorbidity ; Consent Forms ; Diagnosis ; Financing, Organized ; Humans ; Intensive Care Units* ; Male ; Medical Records ; Respiratory Insufficiency ; Retrospective Studies ; Terminal Care ; Terminally Ill

Purpose: Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists’ performance and provides results equivalent or superior to those of radiologists’ alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk. Methods Approximately 32,714 participants will be enrolled between February 2021 and December 2022 at 5 study sites in Korea. A radiologist specializing in breast imaging will interpret the mammography readings with or without the use of AI-based CADe/x. If recall is required, further diagnostic workup will be conducted to confirm the cancer detected on screening. The findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within both 1 year and 2 years of screening. The national cancer registry database will be reviewed in 2026 and 2027, and the results of this study are expected to be published in 2027. In addition, the diagnostic accuracy of general radiologists and radiologists specializing in breast imaging from another hospital with or without the use of AI-based CADe/x will be compared considering mammography readings for breast cancer screening.DiscussionThe Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM) study is a prospective multicenter study that aims to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of women with average breast cancer risk. AI-STREAM is currently in the patient enrollment phase.Trial RegistrationClinicalTrials.gov Identifier: NCT05024591