Radiation-induced lung injury (RILI) is a progressive inflammatory process commonly seen following irradiation for lung cancer. The disease can be insidious, often characterized by acute pneumonitis followed by chronic fibrosis with significant associated morbidity. No therapies are approved for RILI, and accurate disease quantification is a major barrier to improved management.
Patients with Li-Fraumeni syndrome (LFS) are prone to develop a variety of malignancies due to insufficient activity of the encoded tumor suppressor protein P53, including adrenocortical carcinoma, breast cancer, lung cancer, pancreatic cancer, and sarcoma. In the setting of LFS, local treatment options for lung metastases are limited to surgery and thermal ablation since radiotherapy and some systemic therapies predispose patients to additional future malignancies. We present the case of a 45-year-old woman with LFS with leiomyosarcoma metastases to both lungs who underwent bilateral wedge resections to treat a total of eight lung metastases followed by six percutaneous cryoablation sessions to treat 15 additional lung metastases over a period of 24 months. Our case demonstrates the option of multimodal local ablative therapies for lung metastases in patients with LFS, including percutaneous cryoablation.
Summary Techniques of artificial intelligence (AI) are increasingly used in the treatment of patients, such as providing a diagnosis in radiological imaging, improving workflow by triaging patients or providing an expert opinion based on clinical symptoms; however, such AI techniques also hold intrinsic risks as AI algorithms may point in the wrong direction and constitute a black box without explaining the reason for the decision-making process. This article outlines a case where an erroneous ChatGPT diagnosis, relied upon by the patient to evaluate symptoms, led to a significant treatment delay and a potentially life-threatening situation. With this case, we would like to point out the typical risks posed by the widespread application of AI tools not intended for medical decision-making.
Objective: This study develops a BI-RADS-like scoring system for vascular microcalcifications in mammographies, correlating breast arterial calcification (BAC) in a mammography with coronary artery calcification (CAC), and specifying differences between microcalcifications caused by BAC and microcalcifications potentially associated with malignant disease. Materials and Methods: This retrospective single-center cohort study evaluated 124 consecutive female patients (with a median age of 57 years). The presence of CAC was evaluated based on the Agatston score obtained from non-enhanced coronary computed tomography, and the calcifications detected in the mammography were graded on a four-point Likert scale, with the following criteria: (1) no visible or sporadically scattered microcalcifications, (2) suspicious microcalcification not distinguishable from breast arterial calcification, (3) minor breast artery calcifications, and (4) major breast artery calcifications. Inter-rater agreement was assessed in three readers using the Fleiss’ kappa, and the correlation between CAC and BAC was evaluated using the Spearman’s rank-order and by the calculation of sensitivity/specificity. Results: The reliability of the visual classification of BAC was high, with an overall Fleiss’ kappa for inter-rater agreement of 0.76 (ranging between 0.62 and 0.89 depending on the score). In 15.1% of patients, a BAC score of two was assigned indicating calcifications indistinguishable regarding vascular or malignant origin. In 17.7% of patients, minor or major breast artery calcifications were found (BAC 3–4). BAC was more prevalent among the patients with CAC (p < 0.001), and the severity of CAC increased with the BAC score; in the group with a BAC score of one, 15% of patients exhibited mild and severe CAC, in those with a BAC of two, this was 31%, in those with BAC of three, this was 38%, and in those with a BAC of four, this was 44%. The sensitivity for detecting CAC, based on the mammographic BAC score, was 30.3% at a specificity of 96.7%. Conclusions: The standardized visual grading of BAC in mammographies on a four-point scale is feasible with substantial interobserver agreement, potentially improving the treatment of patients with suspicious microcalcifications and CAC.
