Adenocarcinoma ; pathology ; Humans ; Lung Neoplasms ; pathology

Humans ; Adenocarcinoma/pathology* ; Lung Neoplasms/pathology* ; Lung/pathology*

Adenocarcinoma of Lung ; Adenocarcinoma, Clear Cell ; Carcinoma, Renal Cell/pathology* ; Humans ; Kidney Neoplasms/pathology* ; Lung Neoplasms/pathology*

Lung cancer is the leading cause of cancer death in the world today, and adenocarcinoma is the most common histopathological type of lung cancer. In May 2021, World Health Organization (WHO) released the 5th edition of the WHO classification of thoracic tumors, which classifies invasive non-mucinous adenocarcinoma (INMA) into lepidic adenocarcinoma, acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma, and micropapillary adenocarcinoma based on its histological characteristics. These five pathological subtypes differ in clinical features, treatment and prognosis. A complete understanding of the characteristics of these subtypes is essential for the clinical diagnosis, treatment options, and prognosis predictions of patients with lung adenocarcinoma, including recurrence and progression. This article will review the grading system, morphology, imaging prediction, lymph node metastasis, surgery, chemotherapy, targeted therapy and immunotherapy of different pathological subtypes of INMA.
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Humans ; Lung Neoplasms/pathology* ; Adenocarcinoma of Lung/pathology* ; Adenocarcinoma/pathology* ; Prognosis ; Lymphatic Metastasis ; Neoplasm Staging ; Retrospective Studies

Humans ; Adenocarcinoma of Lung/pathology* ; Adenocarcinoma/pathology* ; Lung Neoplasms/pathology* ; Neoplasm Staging ; Prognosis ; Retrospective Studies ; Adenocarcinoma, Papillary/pathology*

BACKGROUND: Invasive mucinous adenocarcinoma (IMA) was a rare and specific type of lung adenocarcinoma, which was often characterized by fewer lymphatic metastases. Therefore, it was difficult to evaluate the prognosis of these tumors based on the existing tumor-node-metastasis (TNM) staging. So, this study aimed to develop Nomograms to predict outcomes of patients with pathologic N0 in resected IMA. METHODS: According to the inclusion criteria and exclusion criteria, IMA patients with pathologic N0 in The Affiliated Lihuili Hospital of Ningbo University (training cohort, n=78) and Ningbo No.2 Hospital (validation cohort, n=66) were reviewed between July 2012 and May 2017. The prognostic value of the clinicopathological features in the training cohort was analyzed and prognostic prediction models were established, and the performances of models were evaluated. Finally, the validation cohort data was put in for external validation. RESULTS: Univariate analysis showed that pneumonic type, larger tumor size, mixed mucinous/non-mucinous component, and higher overall stage were significant influence factors of 5-year progression-free survival (PFS) and overall survival (OS). Multivariate analysis further indicated that type of imaging, tumor size, mucinous component were the independent prognostic factors for poor 5-year PFS and OS. Moreover, the 5-year PFS and OS rates were 62.82% and 75.64%, respectively. In subgroups, the survival analysis also showed that the pneumonic type and mixed mucinous/non-mucinous patients had significantly poorer 5-year PFS and OS compared with solitary type and pure mucinous patients, respectively. The C-index of Nomograms with 5-year PFS and OS were 0.815 (95%CI: 0.741-0.889) and 0.767 (95%CI: 0.669-0.865). The calibration curve and decision curve analysis (DCA) of both models showed good predictive performances in both cohorts. CONCLUSIONS The Nomograms based on clinicopathological characteristics in a certain extent, can be used as an effective prognostic tool for patients with pathologic N0 after IMA resection.
Humans ; Prognosis ; Lung Neoplasms/pathology* ; Adenocarcinoma, Mucinous/pathology* ; Adenocarcinoma of Lung/pathology* ; Neoplasm Staging ; Lung/pathology* ; Retrospective Studies

The International Agency for Research on Cancer (IARC) published the World Health Organization (WHO) classification of thoracic tumors (5th edition) in May 2021, only six years after the 4th edition of WHO Classification. With the application of low-dose spiral computed tomography (CT) as an early screening method for lung tumors in recent years, lung adenocarcinoma has become the main type of disease in many hospital surgical treatments. The WHO classification serves as the authoritative guide for pathological diagnosis, and any slight change in the classification is at the heart of pathologists, clinicians and patients. Adenocarcinoma in situ is a newly added type of adenocarcinoma diagnosis in the 4th edition of the WHO classification, and it is also the focus of clinical treatment and research at home and abroad in recent years. Because its catalog position has been adjusted in the 5th edition of the WHO classification, there has been a huge controversy and discussion among clinicians and patients that "adenocarcinoma in situ was excluded from the category of malignant tumors". This article will briefly explain the origin of the diagnosis of lung adenocarcinoma in situ, the adjustment of the new classification catalog, and whether adenocarcinoma in situ is benign or malignant.
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Adenocarcinoma in Situ/pathology* ; Adenocarcinoma of Lung/diagnostic imaging* ; Humans ; Lung Neoplasms/pathology* ; Neoplasm Staging

Ground-glass nodule (GGN) lung cancer often progresses slowly in clinical and there are few clinical studies on long-term follow-up of patients with operable GGN lung cancer treated with stereotactic body radiation therapy (SBRT). We present a successful case of GGN lung cancer treated with SBRT, but a new GGN was found in the lung adjacent to the SBRT target during follow-up. The nodule progressed rapidly and was confirmed as lung adenocarcinoma by surgical resection. No significant risk factors and related driving genes were found in molecular pathological findings and genetic tests. It deserves further study whether new GGN is related to the SBRT. This case suggests that the follow-up after SBRT should be vigilant against the occurrence of new rapidly progressive lung cancer in the target area and adjacent lung tissue.
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Humans ; Lung Neoplasms/pathology* ; Radiosurgery ; Retrospective Studies ; Adenocarcinoma of Lung/surgery* ; Lung/pathology*

BACKGROUND: Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules. METHODS: Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed. RESULTS: In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively. CONCLUSIONS Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.
Adenocarcinoma/pathology* ; Adenocarcinoma of Lung/pathology* ; Artificial Intelligence ; Humans ; Lung Neoplasms/pathology* ; Multiple Pulmonary Nodules ; Neoplasm Invasiveness ; Retrospective Studies

Adenocarcinoma ; classification ; genetics ; pathology ; Forecasting ; Humans ; Lung ; pathology ; Lung Neoplasms ; classification ; genetics ; pathology