Objective:The possible enhancing effect of anlotinib on programmed death receptor ligand(PD-L1)antibody and the efficacy-predicting power of PD-L1 in micro-conduit endothelium,including lymphatic endothelial cells(LEC...Objective:The possible enhancing effect of anlotinib on programmed death receptor ligand(PD-L1)antibody and the efficacy-predicting power of PD-L1 in micro-conduit endothelium,including lymphatic endothelial cells(LECs)and blood endothelial cells(BECs),were determined to identify patients who would benefit from this treatment.Methods:PD-L1 positivity in LECs,BECs,and tumor cells(TCs)was assessed using paraffin sections with multicolor immunofluorescence in an investigator’s brochure clinical trial of TQB2450(PD-L1 antibody)alone or in combination with anlotinib in patients with non-small cell lung cancer.Progression-free survival(PFS)with different levels of PD-L1 expression was compared between the two groups.Results:Among 75 patients,the median PFS(mPFS)was longer in patients who received TQB2450 with anlotinib[10 and 12 mg(161 and 194 days,respectively)]than patients receiving TQB2450 alone(61 days)[hazard ratio(HR)_(10 mg)=0.390(95%confidence interval{CI},0.201–0.756),P=0.005;HR_(12 mg)=0.397(0.208–0.756),P=0.005].The results were similar among 58 patients with high PD-L1 expression in LECs and TCs[159 and 209 vs.82 days,HR_(10 mg)=0.445(0.210–0.939),P=0.034;HR_(12 mg)=0.369(0.174–0.784),P=0.009],and 53 patients with high PD-L1 expression in BECs and TCs[161 and 209 vs.41 days,HR_(10 mg)=0.340(0.156–0.742),P=0.007;HR_(12 mg)=0.340(0.159–0.727),P=0.005].No differences were detected in the mPFS between the TQB2450 and combination therapy groups in 13 low/no LEC-expressing and 18 low/no BEC-expressing PD-L1 cases.Conclusions:Mono-immunotherapy is not effective in patients with high PD-L1 expression in LECs and/or BECs.Anlotinib may increase efficacy by downregulating PD-L1 expression in LECs and/or BECs,which is presumed to be a feasible marker for screening the optimal immune patient population undergoing anti-angiogenic therapy.展开更多
In 1889, Paget proposed a 'Seed-and-Soil' theory for cancer metastasis. According to this theory, cancer metastasis is not random, and one remote organ is more prone to be the seat of secondary tumor growth th...In 1889, Paget proposed a 'Seed-and-Soil' theory for cancer metastasis. According to this theory, cancer metastasis is not random, and one remote organ is more prone to be the seat of secondary tumor growth than another.展开更多
Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed di...Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt.Here,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.Methods:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation.Briefly,a total of 28,966 qualified images were collected.Among these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple randomiza-tion.Additionally,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist evaluation.The dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the experts.Results:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign diseases.The eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test set.In the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background,with an average DSC of 0.78±0.24 and 0.75±0.26 in the test and prospective test sets,respectively.Conclusions:The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant,and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.展开更多
Cancer is a leading cause of death in China with an estima- tion of nearly 2 million deaths every year （Chen and Fu, 2011b）. Matter of a public health importance in China and worldwide, the scientific community is s...Cancer is a leading cause of death in China with an estima- tion of nearly 2 million deaths every year （Chen and Fu, 2011b）. Matter of a public health importance in China and worldwide, the scientific community is still facing many obstacles to eradicate cancer： complexity of a mul- ti-factorial disease with organ-based specificities, high fail- ure rate of many anti-cancer drugs in clinical trials, lack of understanding of the cancer genesis factors.展开更多
文摘Objective:The possible enhancing effect of anlotinib on programmed death receptor ligand(PD-L1)antibody and the efficacy-predicting power of PD-L1 in micro-conduit endothelium,including lymphatic endothelial cells(LECs)and blood endothelial cells(BECs),were determined to identify patients who would benefit from this treatment.Methods:PD-L1 positivity in LECs,BECs,and tumor cells(TCs)was assessed using paraffin sections with multicolor immunofluorescence in an investigator’s brochure clinical trial of TQB2450(PD-L1 antibody)alone or in combination with anlotinib in patients with non-small cell lung cancer.Progression-free survival(PFS)with different levels of PD-L1 expression was compared between the two groups.Results:Among 75 patients,the median PFS(mPFS)was longer in patients who received TQB2450 with anlotinib[10 and 12 mg(161 and 194 days,respectively)]than patients receiving TQB2450 alone(61 days)[hazard ratio(HR)_(10 mg)=0.390(95%confidence interval{CI},0.201–0.756),P=0.005;HR_(12 mg)=0.397(0.208–0.756),P=0.005].The results were similar among 58 patients with high PD-L1 expression in LECs and TCs[159 and 209 vs.82 days,HR_(10 mg)=0.445(0.210–0.939),P=0.034;HR_(12 mg)=0.369(0.174–0.784),P=0.009],and 53 patients with high PD-L1 expression in BECs and TCs[161 and 209 vs.41 days,HR_(10 mg)=0.340(0.156–0.742),P=0.007;HR_(12 mg)=0.340(0.159–0.727),P=0.005].No differences were detected in the mPFS between the TQB2450 and combination therapy groups in 13 low/no LEC-expressing and 18 low/no BEC-expressing PD-L1 cases.Conclusions:Mono-immunotherapy is not effective in patients with high PD-L1 expression in LECs and/or BECs.Anlotinib may increase efficacy by downregulating PD-L1 expression in LECs and/or BECs,which is presumed to be a feasible marker for screening the optimal immune patient population undergoing anti-angiogenic therapy.
文摘In 1889, Paget proposed a 'Seed-and-Soil' theory for cancer metastasis. According to this theory, cancer metastasis is not random, and one remote organ is more prone to be the seat of secondary tumor growth than another.
基金supported by the National Natural Science Foundation of China[Grant Nos.81572665,81672680,81472525,81702873]the International Cooperation Project of Science and Technology Plan of Guangdong Province[Grant No.2016A050502011]the Health&Medical Collaborative Innovation Project of Guangzhou City,China(Grant No.201604020003).
文摘Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt.Here,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.Methods:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation.Briefly,a total of 28,966 qualified images were collected.Among these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple randomiza-tion.Additionally,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist evaluation.The dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the experts.Results:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign diseases.The eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test set.In the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background,with an average DSC of 0.78±0.24 and 0.75±0.26 in the test and prospective test sets,respectively.Conclusions:The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant,and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.
文摘Cancer is a leading cause of death in China with an estima- tion of nearly 2 million deaths every year （Chen and Fu, 2011b）. Matter of a public health importance in China and worldwide, the scientific community is still facing many obstacles to eradicate cancer： complexity of a mul- ti-factorial disease with organ-based specificities, high fail- ure rate of many anti-cancer drugs in clinical trials, lack of understanding of the cancer genesis factors.
