Case Report: Long-Term Survival With Anlotinib in a Patient With Advanced Undifferentiated Large-Cell Lung Cancer and Rare Tonsillar Metastasis

Undifferentiated large-cell lung cancer is a rare type of non-small cell lung cancer (NSCLC) with a poor prognosis. It is insensitive to chemotherapy and easily develops drug resistance. Analysis of the Surveillance, Epidemiology, and End Results (SEER) database showed that patients with stage IV undifferentiated large-cell lung cancer had a median overall survival (OS) of only 4 months and that those who received chemotherapy had a median OS of only 5 months longer than those who did not. For the first time, we report a case of advanced large-cell undifferentiated lung cancer with rare tonsil metastasis. The patient developed resistance after 3 months of platinum-based systemic chemotherapy and local treatment. Antiangiogenic therapy has been continuously progressing and has shown certain efficacy in treating many malignant tumors, such as lung cancer. However, there are no relevant studies or case reports on antiangiogenic therapy in the treatment of undifferentiated large-cell lung cancer. Anlotinib, an orally delivered small-molecule antiangiogenic tyrosine kinase inhibitor (TKI), was administered to this patient after chemotherapy resistance occurred, and the outcome was assessed as continued stable disease (SD). As of the last follow-up evaluation, the progression-free survival (PFS) of the patient was 21.5 months, and the OS was 27.5 months. Retrospective immunohistochemical analysis showed that the patient was positive for one of the targets of anlotinib (PDGFR). In general, the findings in this case suggest that anlotinib may be an option with good efficacy for patients with large-cell undifferentiated lung cancer after chemotherapy resistance that may have good efficacy and also suggest that PDGFR may be the target underlying this effect.

Classification and Stage Prediction of Lung Cancer using Convolutional Neural Networks

In recent years, digital image processing is widely used for the medical treatment classification and diagnosis. Lung cancer is the most leading cause of death in all over the world nowadays. Based on the signs and symptoms it can’t be diagnosis and treatment classified at the early stage. However it can be identified through the symptoms like coughing up blood and chest pain, the stages and risk factors of the cancer cannot be identified through the symptoms. The CT scanned lung images should be involved in image classification processing for earlier prediction of stages and treatment diagnosis. In existing, machine learning treatment classification can be done through the SVM classification. In case of large set of training samples, this will not be in accurate manner and it has less accuracy because of improper feature extraction techniques. Thus the performance of the classification based on the segmented features obtained in preceding sections. The extracted fine-grained training data through deep learning are utilized for the classification using Convolution Neural Network (CNN). In this paper, we propose a novel framework to classify both small cell and large cell lung cancer and predict its type and treatment using CNN. It is also concentrates on the preprocessing and segmentation processes to accomplish the accuracy in prediction. The experiment results in Python - TensorFlow with Kaggle image dataset show that compared to state of the art of classification and prediction methods, the proposed scheme can obtain much higher accuracy in type prediction and treatment diagnosis.

Clinicopathological characteristics and cancer-specific survival prediction of Large cell lung cencer: a population-based study

Background:The features and survival outcome large cell lung cancer(LCLC) are scarce reported due to its low incidence,as a result, the prognoses of LCLC remain unclear.The aim of this study was to describe the demographic and clinical characteristics of large cell lung cancer with a population-base database and find the prognosis factors for cancer-specific survival(CSS) of the LCLC patients.Besides,a nomogram would be developed and independently validated to predict the CSS for LCLC based on the found prognosis factors. Methods: We extracted LCLC patients information from the Surveillance, Epidemiology, and End Results(SEER) database(2005-2014) and summarized the characteristic of the extracted factors.We used the Cox proportional hazards regression to find the prognosis factors for LCLC patients and develop the nomogram based on these in a splitted train cohort from the extracted data.The validation of the developed nomogram would be performed in an independent validation cohort from the extracted data, in which the C-index and the average of the time-dependent area under the receiver operating characteristic curve(time-dependent AUC) for CSS in 1-year, 3-year and 5-year would be calculated.The calibration curves would be drawn to visualize the performance of the established nomogram. Results: In result,4936 patients with LCLC were identified from the SEER database. Nearly half of LCLC patients were diagnosis with stage IV,only approximately 20% of patients was performed surgery.The prognosis factors influence the LCLC patients included age, sex,American Joint Committee on Cancer (AJCC) stage,race,surgery, tumour size and marital status.The calculated C-index was 0.701±0.01,mean time-dependent AUC for CSS in 1-year, 3-year and 5-year was 0.88.The calibrate curve showed that the gap between the predicted and observed CSS for 1-year, 3-year and 5-year was small. Conclusions:Sex,age,race,marital status,AJCC stage, surgery and tumour size are all the independent prognostic factors for CSS of the LCLC.The established nomogram can provide more precise evaluation for the survival of LCLC patients,and help the clinicians to make individual management.