Stromal micropapillary pattern predominant lung adenocarcinoma - a report of two cases

Generally, adenocarcinomas with micropapillary pattern, featuring small papillary tufts lacking a central fibrovascular core, are thought to have poor prognosis. This pattern has been described in various organs. However, tumor cells with micropapillary pattern of lung adenocarcinoma are more often seen to float within alveolar spaces (aerogenous micropapillary pattern, AMP) than in fibrotic stroma like other organs (stromal micropapillary pattern, SMP) and SMP predominant lung adenocarcinoma (SMPPLA) has not been well described yet. We presented two cases of SMPPLA which were found in the last four years. Both the cases showed more than 50% of SMP in the tumor area. The majority of the stromal micropapillary clusters expressed MUC1 and epithelial membrane antigen along the outer surface of cell membrane. On the other hand, connective tissues surrounding stromal micropapillary clusters showed no reactivity for epithelial markers (thyroid transcription factor-1 and cytokeratin) or endothelial marker (D2-40 and CD34). It means clusters of SMP do not exist within air space or lymphatic or vessel lumens. The tumors with SMP often presented lymphatic permeation and vessel invasion, and intriguingly, one of the two cases showed metastasis to the mediastinal lymph node. Additionally, both the cases showed EGFR point mutations of exon 21. These results suggest that SMPPLA might be associated with poor prognosis and effective for EGFR tyrosine kinase inhibitors

Can Artificial Intelligence Replace Humans for Detecting Lung Tumors on Radiographs? An Examination of Resected Malignant Lung Tumors

Objective: Although lung cancer screening trials have showed the efficacy of computed tomography to decrease mortality compared with chest radiography, the two are widely taken as different kinds of clinical practices. Artificial intelligence can improve outcomes by detecting lung tumors in chest radiographs. Currently, artificial intelligence is used as an aid for physicians to interpret radiograms, but with the future evolution of artificial intelligence, it may become a modality that replaces physicians. Therefore, in this study, we investigated the current situation of lung cancer diagnosis by artificial intelligence. Methods: In total, we recruited 174 consecutive patients with malignant pulmonary tumors who underwent surgery after chest radiography that was checked by artificial intelligence before surgery. Artificial intelligence diagnoses were performed using the medical image analysis software EIRL X-ray Lung Nodule version 1.12, (LPIXEL Inc., Tokyo, Japan). Results: The artificial intelligence determined pulmonary tumors in 90 cases (51.7% for all patients and 57.7% excluding 18 patients with adenocarcinoma in situ). There was no significant difference in the detection rate by the artificial intelligence among histological types. All eighteen cases of adenocarcinoma in situ were not detected by either the artificial intelligence or the physicians. In a univariate analysis, the artificial intelligence could detect cases with larger histopathological tumor size (p p p p = 0.006). In 156 cases excluding adenocarcinoma in situ, we examined the rate of artificial intelligence detection based on the tumor site. Tumors in the lower lung field area were less frequently detected (p = 0.019) and tumors in the middle lung field area were more frequently detected (p = 0.014) compared with tumors in the upper lung field area. Conclusions: Our study showed that using artificial intelligence, the diagnosis of tumor-associated findings and the diagnosis of areas that overlap with anatomical structures is not satisfactory. While the current standing of artificial intelligence diagnostics is to assist physicians in making diagnoses, there is the possibility that artificial intelligence can substitute for humans in the future. However, artificial intelligence should be used in the future as an enhancement, to aid physicians in the role of a radiologist in the workflow

Congenital cystic adenomatoid malformation in adults: Report of a case presenting with a recurrent pneumothorax and a literature review of 60 cases

Congenital cystic adenomatoid malformation (CCAM) is a congenital pulmonary cystic disease that is mostly detected and diagnosed prenatally or during the neonatal period, while rarely being observed in adults. Here, we report an adult case of CCAM that was diagnosed following surgery for a recurrent pneumothorax. We further review 60 case reports on adult CCAM that have been previously published. The patient was a 29-year-old woman with a severe left pneumothorax. Her computed tomography scan showed the presence of multiple pulmonary cysts at the base of the left lower lobe. Since she had experienced a left pneumothorax twice previously, surgery was indicated. A wedge lung resection of the pulmonary cysts was performed thoracoscopically. The postoperative pathological diagnosis was type I CCAM. From the review, 7 adult CCAM patients (11.7%) out of 61, including the patient in the present case, presented with pneumothorax, while 21 patients (35%) presented with infection. Thirty-nine foci of CCAM (65%) were located in lower lobes. Moreover, malignancies were associated in 8 cases (13.3%). We propose that if multicystic lung lesions are found in pneumothorax patients, particularly in lower lobes, CCAM should be considered during the differential diagnosis, even in adults