Diagnostic Efficacy of CT Radiomic Features in Pulmonary Invasive Mucinous Adenocarcinoma

Retracted: Diagnostic Efficacy of CT Radiomic Features in Pulmonary Invasive Mucinous Adenocarcinoma

[This retracts the article DOI: 10.1155/2022/5314225.].

Invasive mucinous adenocarcinoma misdiagnosed as pneumonia: A case report

Common imaging findings of invasive mucinous adenocarcinoma (IMA) include consolidation of the lung parenchyma, nodules, and ground-glass changes. However, the IMA imaging findings in the present case included diffuse, patchy and blurry density shadows through both lungs. To the best of the authors' knowledge, this image pattern has rarely been reported. The patient provided his consent and authorized the publication of photographs featuring his likeness. The present study reported a patient was diagnosed with IMA via pathologic and genetic analyses. Following antibiotic treatment, the lesions in both sides became larger. Further examinations were completed and IMA was confirmed by biopsy pathohistological examination. Pathological specimens were negative for almost all driver genes mutations, except KRAS. The patients and family refused further treatment, including chemotherapy, radiotherapy and interventional chemotherapy and the patient was discharged from The First Affiliated Hospital of Chengdu Medical College. The present case report emphasized that IMA should be suspected when imaging studies show diffuse lesions throughout both lungs. When a patient does not respond to treatment, clinicians should consider alternative diagnoses.

Primary pulmonary extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue type: a case report and literature review

Primary pulmonary mucosa-associated lymphoid tissue (MALT)-derived lymphoma is a low-grade B-cell non-Hodgkin's lymphoma. It is rare with unclear clinical and imaging findings, requiring biopsy or surgery for diagnosis. Here, we provide a new case to learn the symptoms, diagnosis and treatment of primary pulmonary MALT lymphoma. The patient was a 51-year-old male. During the annual physical examination in 2019, a shadow in the lower lobe of the right lung was accidentally found in his chest computed tomography image. In 2020, the size and density of the shadows increased, which was suspected to be lung adenocarcinoma. The patient underwent video-assisted thoracoscopic surgery and segmental resection. Pathological examination showed residual germinal centers around the tumor cells, and many inflammatory cells had diffusely infiltrated, mainly monocyte-like B cells. Immunohistochemical analysis showed that CD3, CD20, Bcl-2, CD43, CK-pan and CD23 were positive, while BCL-6, CD5, CD10, c-myc and cyclin D1 were negative. The patient was diagnosed with MALT extranodal marginal zone B-cell lymphoma. The patient did not receive chemotherapy or radiotherapy after the operation but was still under close observation. Primary pulmonary MALT develops slowly and tends to be inert and spontaneous. Due to the lack of specific clinical symptoms and imaging findings, it can easily be misdiagnosed as tuberculosis, lung cancer, or infection. Thoracoscopic resection may be a good choice for the diagnosis and treatment.

Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate–thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left–right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate.