LungPath: artificial intelligence-driven histologic pattern recognition for improved diagnosis of early-stage invasive lung adenocarcinoma.

Background: Early-stage invasive lung adenocarcinoma (ADC) characterized by a predominant micropapillary or solid pattern exhibit an elevated risk of recurrence following sub-lobar resection, thus determining histological subtype of early-stage invasive ADC prior surgery is important for formulating lobectomy or sub-lobar resection. This study aims to develop a deep learning algorithm and assess its clinical capability in distinguishing high-risk or low-risk histologic patterns in early-stage invasive ADC based on preoperative computed tomography (CT) scans. Methods: Two retrospective cohorts were included: development cohort 1 and external test cohort 2, comprising patients diagnosed with T1 stage invasive ADC. Electronic medical records and CT scans of all patients were documented. Patients were stratified into two risk groups. High-risk group: comprising cases with a micropapillary component ≥5% or a predominant solid pattern. Low-risk group: encompassing cases with a micropapillary component <5% and an absence of a predominant solid pattern. The overall segmentation model was modified based on Mask Region-based Convolutional Neural Network (Mask-RCNN), and Residual Network 50 (ResNet50)_3D was employed for image classification. Results: A total of 432 patients participated in this study, with 385 cases in cohort 1 and 47 cases in cohort 2. The fine-outline results produced by the auto-segmentation model exhibited a high level of agreement with manual segmentation by human experts, yielding a mean dice coefficient of 0.86 [95% confidence interval (CI): 0.85-0.87] in cohort 1 and 0.84 (95% CI: 0.82-0.85) in cohort 2. Furthermore, the deep learning model effectively differentiated the high-risk group from the low-risk group, achieving an area under the curve (AUC) of 0.89 (95% CI: 0.88-0.90) in cohort 1. In the external validation conducted in cohort 2, the deep learning model displayed an AUC of 0.87 (95% CI: 0.84-0.88) in distinguishing the high-risk group from the low-risk group. The average diagnostic time was 16.00±3.2 seconds, with an accuracy of 0.82 (95% CI: 0.81-0.83). Conclusions: We have developed a deep learning algorithm, LungPath, for the automated segmentation of pulmonary nodules and prediction of high-risk histological patterns in early-stage lung ADC based on CT scans.

Neoadjuvant camrelizumab plus chemotherapy for resectable, locally advanced esophageal squamous cell carcinoma (NIC-ESCC2019): A multicenter, phase 2 study.

Optimal treatment for resectable esophageal squamous cell carcinoma (ESCC) is controversial, especially in the context of potential benefit of combining PD-1 blockade with neoadjuvant therapy. This phase 2 study aimed to assess neoadjuvant camrelizumab plus chemotherapy in this population. Patients (clinical stage II-IVA) received two cycles of neoadjuvant chemoimmunotherapy (NIC) with camrelizumab (200 mg on day 1) plus nab-paclitaxel (260 mg/m2 in total on day 1 and day 8) and cisplatin (75 mg/m2 in total on days 1-3) of each 21-day cycle. Surgery was performed approximately 6 weeks after completion of NIC. Primary endpoint was complete pathologic response (CPR) rate in primary tumor. Secondary endpoints were objective response rate (ORR) per RECIST v1.1, 2-year progression-free survival (PFS) rate after surgery, PFS, overall survival (OS) and safety during NIC and perioperative period. Between 17 January 2020 and 8 December 2020, 56 patients were enrolled, and 51 received esophagectomy. Data cutoff date was 25 August 2021. The CPR rate was 35.3% (95% CI, 21.7%-48.9%). NIC had an ORR of 66.7% (95% CI, 40.0%-70.4%) and treatment-related adverse events (TRAEs) of low severity (grade 1-2, 75.0%; grade 3, 10.7%; grade 4-5, no). No perioperative mortality occurred. Three (5.9%) patients had tumor recurrence and one (2.0%) patient died. The 2-year PFS rate, median PFS and median OS had not been reached yet. Camrelizumab plus neoadjuvant chemotherapy in resectable ESCC demonstrates promising efficacy with acceptable toxicity, providing a feasible and effective option. Study is ongoing for long-term survival analyses.

Impact of metformin on survival outcome of esophageal squamous cell carcinomas patients undergoing surgical resection: a multicenter retrospective study.

BACKGROUND: Diabetes mellitus is a recognized risk factor for esophageal squamous cell carcinomas (ESCC), and metformin is a recognized protective factor for some gastrointestinal tumors. But knowledge is limited regarding the effect of metformin on survival outcome of ESCC patients with type 2 diabetes mellitus (T2DM). We assessed the impact of post-diagnosis metformin use on overall survival (OS) and disease-free survival (DFS) in ESCC with T2DM undergoing surgical resection. METHODS: A retrospective analysis was performed on 3,523 patients with ESCC who met the study conditions after surgical resection. Log-rank and Cox regression models were used to evaluate the relationship between metformin and T2DM and ESCC survival rate, and adjusted according to age, gender, BMI, smoking, drinking and staging, et al. RESULTS: Among included ESCC patients, 619 were associated with type 2 diabetes, while the remaining 2,904 were not associated with type 2 diabetes. The 5-year OS (28.43%) of patients with T2DM was significantly lower than that of patients without T2DM (32.75%), P=0.037. DFS in 5 years were 27.30% (with T2DM) and 31.75% (without T2DM) (P=0.030), respectively. Compared with patients without T2DM, patients with T2DM presented worse OS [adjusted risk ratio (HRadj) =1.19] and DFS (HRadj =1.17; P<0.001). Among the 619 patients with type 2 diabetes, 485 were treated with metformin and 134 were not treated with metformin. Patients treated with metformin had significantly improved OS [adjusted risk ratio (HRadj) =0.89; P=0.031) and DFS (HRadj =0.90; P=0.013). CONCLUSIONS: T2DM was again associated with poorer survival in ESCC patients, and metformin may improve the prognosis of these patients.

The regulatory and predictive functions of miR-17 and miR-92 families on cisplatin resistance of non-small cell lung cancer.

BACKGROUND: Chemotherapy is an important therapeutic approach for non-small cell lung cancer (NSCLC). However, a successful long-term treatment can be prevented by the occurring of chemotherapy resistance frequently, and the molecular mechanisms of chemotherapy resistance in NSCLC remain unclear. In this study, abnormal expressions of miR-17 and miR-92 families are observed in cisplatin-resistant cells, suggesting that miR-17 and miR-92 families are involved in the regulation of cisplatin resistance in NSCLC. METHODS: miRNA microarray shows that miR-17 and miR-92 families are all down-regulated in cisplatin-resistant A549/DDP cells compared with cisplatin-sensitive A549 cells. The aim of this study is to investigate the regulatory functions of miR-17 and miR-92 families on the formation of cisplatin resistance and the predictive functions of them as biomarkers of platinum-based chemotherapy resistance in NSCLC. RESULTS: The low expressions of miR-17 and miR-92 families can maintain cisplatin resistance through the regulation of CDKN1A and RAD21. As a result of high expressions of CDKN1A and RAD21, the inhibition of DNA synthesis and the repair of DNA damage are achieved and these may be two major contributing factors to cisplatin resistance. Moreover, we demonstrate that the expressions of miR-17 and miR-92 families in NSCLC tissues are significantly associated with platinum-based chemotherapy response. CONCLUSION: Our study indicates that miR-17 and miR-92 families play important roles in cisplatin resistance and can be used as potential biomarkers for better predicting the clinical response to platinum-based chemotherapy in NSCLC.

MiRNA 17 family regulates cisplatin-resistant and metastasis by targeting TGFbetaR2 in NSCLC.

MicroRNAs (miRNAs) have been proven to play crucial roles in cancer, including tumor chemotherapy resistance and metastasis of non-small-cell lung cancer (NSCLC). TGFß signal pathway abnormality is widely found in cancer and correlates with tumor proliferation, apoptosis and metastasis. Here, miR-17, 20a, 20b were detected down-regulated in A549/DDP cells (cisplatin resistance) compared with A549 cells (cisplatin sensitive). Over-expression of miR-17, 20a, 20b can not only decrease cisplatin-resistant but also reduce migration by inhibiting epithelial-to-mesenchymal transition (EMT) in A549/DDP cells. These functions of miR-17, 20a, 20b may be caused at least in part via inhibition of TGFß signal pathway, as miR-17, 20a, 20b are shown to directly target and repress TGF-beta receptor 2 (TGFßR2) which is an important component of TGFß signal pathway. Consequently, our study suggests that miRNA 17 family (including miR-17, 20a, 20b) can act as TGFßR2 suppressor for reversing cisplatin-resistant and suppressing metastasis in NSCLC.