CN111933287A - Integration of platelet characteristics in blood and platelet-rich plasma to establish lung cancer models - Google Patents
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Abstract
The invention discloses a method for establishing a lung cancer model by integrating platelet characteristics in blood and platelet-rich plasma, which comprises the following steps: s01, collecting whole blood samples of all participants; s02, measuring blood routine data by a counter; s02, centrifuging to obtain platelet-rich plasma in the residual blood; s03, measuring blood routine data in the platelet rich plasma; s04, comparing the data of the training set through a rank sum test pair to obtain a differential variable; s05, establishing a regression development diagnosis model through the differential variables; s06, analyzing characteristic (ROC) curves in the training set and the testing set; s07, selecting salient features in a feature (ROC) curve in a training set, and incorporating the salient features into a regression development diagnosis model; s08, evaluating the performance of the regression development diagnosis model through the R2 statistical data and the quantitative discrimination of Nagelkerke; s09, evaluating the sensitivity, specificity and corresponding 95% confidence intervals of the training set; s10, repeating the step S07-S09 to the evaluation data of the test group in the test group.
Description
Technical Field
The invention relates to the field of medical treatment, in particular to a method for establishing a lung cancer model by integrating platelet characteristics in blood and platelet-rich plasma.
Background
Lung cancer has become a leading cause of cancer-related death worldwide. Lung cancer has become one of the most common cancers in china. The incidence of lung cancer is increasing, and nearly 400 million new lung cancer patients are diagnosed each year. It is reported that more than one million people die of lung cancer every year. Nevertheless, mortality in high-risk lung cancer patients screened using low-dose computed tomography (LDCT) was reduced by 20%. Indeed, early detection of cancer may be associated with higher survival rates. However, there is still uncertainty in using LDCT because LDCT identifies both benign and cancerous as nodules. Early diagnosis of lung cancer remains difficult and there are no well-established biomarkers in the blood.
Disclosure of Invention
Aiming at the problems, the invention provides a method for diagnosing lung cancer by the characteristics of blood platelets in blood, which has the advantages of low cost, rapidness, no wound and capability of monitoring at any time.
The technical scheme of the invention is as follows:
a method for modeling lung cancer by integrating platelet characteristics in blood and platelet rich plasma, comprising the steps of:
s01, collecting whole blood samples of all participants and grouping the whole blood samples, wherein the groups comprise a training group and a testing group;
s02, measuring the platelet count, the average platelet volume, the platelet distribution width and the platelet volume by a counter;
s02, centrifuging to obtain platelet-rich plasma in the residual blood;
s03, measuring the platelet count, the average platelet volume, the platelet distribution width and the platelet volume in the platelet-rich plasma;
s04, comparing the data of the training set through a U test and a rank sum test pair to obtain different variables;
s05, establishing a regression development diagnosis model through the variables with the differences in the training group;
s06, analyzing characteristic (ROC) curves in the training set and the testing set;
s07, selecting salient features in a feature (ROC) curve in a training set, and once the salient features are reported, incorporating the salient features into a regression development diagnosis model;
S08R by Nagelkerke2The performance of the regression development diagnosis model is evaluated by statistical data and quantitative discrimination;
s09, evaluating the sensitivity, the specificity and the corresponding 95% confidence interval of the training group, and carrying out intra-group comparison to obtain the probability of lung cancer;
and S10, repeating the steps S07-S09 in the test group, and comparing the probability of the test group with the data of the training group for verification.
In a further embodiment, in step S02, the platelet count, the mean platelet volume, the width of the platelet distribution and the platelet volume are determined by a Mindray Coulter counter 6600.
In a further embodiment, in step S03, the platelet count, the mean platelet volume, the width of the platelet distribution and the platelet volume in the platelet rich plasma are all measured by the Mindray Coulter counter 6600.
In a further embodiment, the regression development diagnosis model in step S05 is a logistic regression model, and its formula is: logit (p) ═ 16.30380+ coefficient of platelet count × platelet count + coefficient of average platelet volume × average platelet volume-coefficient of platelet count in platelet-rich plasma × coefficient of platelet count-average platelet volume in platelet-rich plasma × coefficient of average platelet volume in platelet-rich plasma + coefficient of platelet recovery rate × platelet recovery rate; all coefficients were obtained by logistic regression.
In a further aspect, R of Nagelkerke in step S082For quantifying the predictive strength of regression developed diagnostic models.
The invention has the beneficial effects that:
1. platelet characterization without radioactivity and invasiveness compared to imaging and pathology examinations;
2. the cost is low, the parameters required by the model are easy to obtain, and the burden of a patient can be reduced;
3. the method is rapid, and only 30 minutes are needed from blood sampling to prediction result;
4. accurately, the area under the ROC curve for distinguishing the lung cancer from the normal person is close to the results reported in the prior literature.
Drawings
FIG. 1 is a boxplot analysis of different sets of platelet parameters in training cohorts in the establishment of lung cancer models by integration of platelet characteristics in blood and platelet-rich plasma according to the invention; wherein (a-C) platelet count in whole blood sample, MPV, PDW level, (D-F) platelet count in platelet rich plasma sample, MPV, PDW level, (G) PRR level of normal and malignant participants, (H) receiver working profile of performance of all platelet parameters in training cohort;
FIG. 2 is a diagram of ROC curve analysis in the integrated platelet characterization in blood and platelet rich plasma for the lung cancer model according to the present invention, wherein (A, C) receiver operating characteristic curves of the model in training cohort (A) and test cohort (C) and (B, D) box plot represents the prognostic probability training set (B) and test set (D) calculated by the model from normal and malignant sets;
FIG. 3 is a block diagram of a lung cancer model constructed by integrating platelet characteristics in blood and platelet rich plasma according to the present invention showing the probability of prognosis calculated by the model from the test cohort. Wherein (cohort (a) has prognostic probability levels for participants at different tumor stages, (B) with and without metastasis, ((C) prognostic probability levels for different histological participants (D) prognostic probability levels for participants at nodule size, ((0.05), ((0.01), ((0.001) ((0.0001)).
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
Example (b):
a method for modeling lung cancer by integrating platelet characteristics in blood and platelet rich plasma, comprising the steps of:
s01, collecting whole blood samples of all participants and grouping the whole blood samples, wherein the groups comprise a training group and a testing group;
s02, counter determination of platelet count (bPLT), mean platelet volume mpv (bmpv), platelet distribution width pdw (bpdw) and platelet volume pct (bpct);
s02, centrifuging to obtain Platelet Rich Plasma (PRP) in the residual blood;
s03, measuring platelet count (plt), mean platelet volume mpv (pmpv), platelet distribution width pdw (pdw) and platelet volume pct (pptc) in Platelet Rich Plasma (PRP);
s04, comparing the data of the training set through a rank sum test pair to obtain a differential variable;
s05, establishing a regression development diagnosis model through the variables with the differences in the training group;
s06, analyzing characteristic (ROC) curves in the training set and the testing set;
s07, selecting salient features in a feature (ROC) curve in a training set, and once the salient features are reported, incorporating the salient features into a regression development diagnosis model;
S08R by Nagelkerke2The performance of the regression development diagnosis model is evaluated by statistical data and quantitative discrimination;
s09, evaluating the sensitivity, the specificity and the corresponding 95% confidence interval of the training group, and carrying out intra-group comparison to obtain the probability of lung cancer;
and S10, repeating the steps S07-S09 in the test group, and comparing the probability of the test group with the data of the training group for verification.
In another example, platelet count (bPLT), mean platelet volume mpv (bmpv), platelet distribution width pdw (bptw), and platelet volume pct (bptc) are all determined by Mindray Coulter counter 6600 in step S02.
In another example, platelet count (plt), mean platelet volume mpv (pmpv), platelet distribution width pdw (pdw), and platelet volume pct (pct) in Platelet Rich Plasma (PRP) are all determined by Mindray Coulter counter 6600 in step S03.
In another embodiment, the regression developed diagnostic model in step S05 is a logistic regression model having the formula: logit (p) ═ 16.30380+ coefficient of platelet count × platelet count + coefficient of average platelet volume × average platelet volume-coefficient of platelet count in platelet-rich plasma × coefficient of platelet count-average platelet volume in platelet-rich plasma × coefficient of average platelet volume in platelet-rich plasma + coefficient of platelet recovery rate × platelet recovery rate; all coefficients were obtained by logistic regression.
In another embodiment, R of Nagelkerke in step S082Used to quantify the predictive strength of regression developed diagnostic models.
The specific operation process is as follows:
EDTA anticoagulated blood was collected from all participants. Platelet count (bPLT), mpv (bmpv), pdw (bpdw) and pct (bptc) were determined in fresh whole blood samples using a Mindray Coulter counter 6600. The remaining blood was centrifuged to obtain PRP. Platelet counts (pPLT), MPV (pMPV), PDW (pPDW) and PCT (pPCT) in PRP samples were also determined using the Mindray Coulter counter 6600. All manipulations must be gentle to prevent platelet activation and aggregation. The performance of the merry coulter 6600 counter is acceptable based on daily quality control assessments and comparisons of different devices per week. The results of the laboratory external quality assessments reached 91 to 100 points in 2019.
Statistical analysis
All analyses were performed using SPSS 22.0 software (SPSS Inc., Chicago, Ill.) and R (R statistical calculation Foundation, http:// www.R-project. org.). Data from patients were compared to healthy controls using the U-test and rank-sum test. Values are expressed as mean ± standard deviation, and p <0.05 on both sides is considered significant. Receiver Operating Characteristic (ROC) curves are used to assess the diagnostic role of platelet characteristics in differentiating between lung cancer patients and normal patients. The diagnostic accuracy of the ROC curve is determined by the area under the curve (AUC).
Developing diagnostic models
For the training cohort, a diagnostic model was developed using Logistic regression. Features of significance in the training cohort, once significantly reported, were included in the Logistic regression model. Model performance was evaluated by Nagelkerke's R2 statistics and quantitative discrimination. R2 from Nagelkerke can be used to quantify the predictive strength of the diagnostic model. AUC is used to differentiate performance. Sensitivity, specificity and corresponding 95% confidence intervals were evaluated. The ROC curve is a plot of sensitivity versus 1 specificity. Additionally, the boxplot indicates the predicted probability using a diagnostic model. Using this model, we evaluate and demonstrate the performance of the test queue in the same manner.
Results
The characteristics of the patients are as follows: the study included 159 lung cancer patients and 86 normal participants, strictly following exclusion and inclusion criteria. The 245 individuals were randomly divided into a training group and a test group. Table 1 shows the baseline characteristics of the training and test subjects. A total of 114 lung cancer patients and 57 normal participants participated in the training cohort, while 45 lung cancer patients and 29 normal participants participated in the testing cohort. There were no significant differences in age, gender and smoking status between lung cancer patients and normal participants in the training and testing cohorts.
Table 1. baseline characteristics of training and testing cohorts.
Other tumors
Platelet characterization in whole blood and PRP samples
Platelet feature values in the training cohort are first determined. Mean levels of pPLT (130. + -. 88.2,. times.109/L), pMPV (7.53. + -. 0.15, fl) and platelet recovery (PRR ═ v represents total volume of whole blood) were significantly reduced in the malignant tumors compared to the normal group. Patient (fig. 1) (36.0 ± 1.54% >). There was no difference in pdw compared to the normal group. However, in the whole blood samples, there was no significant difference between the levels of bPLT (209. + -. 6.71,. times.109/L) and bPDW (16.3. + -. 0.040,%) except for bMPV (11.2. + -. 0.17, fl). Malignant and normal populations.
Figure 1 shows ROC using platelet characteristics only in differential diagnosis of lung cancer patients and normal subjects. The AUC for pPLT was 0.69 (95% CI: 0.612-0.772), 0.68 (95% CI: 0.590-0.753) for pMPV, 0.73 (95% CI: 0.647-0.800) for PRR, and 0.64 (95% CI: 0.556-0.725) for bMPV. These results indicate that these features are likely to distinguish lung cancer patients from normal participants.
Fig. 1 boxplot analysis of different sets of platelet parameters in the training cohort. (A-C) platelet count, MPV, PDW levels in whole blood samples. (D-F) platelet count, MPV, PDW levels in platelet rich plasma samples. (G) PRR levels in normal and malignant participants. (H) Receiver working profiles of performance of all platelet parameters in the training cohort were trained. P <0.05, P <0.01, P <0.001, P < 0.0001.
Logistic regression model prediction of lung cancer based on the results of the training cohort, a diagnostic model was developed to assess whether the combination of markers could optimize the separation of the group with lung cancer from the group without cancer. The model was included for bMPV, pPLT, pMPV and PRR, as these features were significantly different from the normal group. Since bPLT was a diagnostic factor in previous studies to identify lung cancer, it also entered this model. A formula is created to calculate the probability: logit (p) -16.30380+0.04586 × bpplt + 1.71527 × bMPV-0.03078 × ppplt-1.44092 × pMPV +0.12968 × PRR, R2 of the diagnostic model of nagelkere is 0.9264131. According to the logistic regression model, we performed ROC curve analysis in the training and test queues (fig. 2). For the training cohort, the AUC was 0.87 (95% CI 0.809-0.924, p924, p <0.0001). The prediction probability of the lung cancer group calculated by the diagnostic model was significantly higher than that of the normal group (p < 0.0001)). The diagnostic model also well separated the probability distribution of lung cancer cases and controls (AUC ═ 0.77[ 95% CI 0.662-0.877], p <0.0001), sensitivity 64%, and specificity 90%. The predicted probability indicates that the model does not distinguish well between advanced stage (stage III-IV) patients and limited stage (stage I-II) disease (FIG. 3A)). There was no significant difference between metastatic and non-metastatic patients, as was the case with LACC and LSCC patients (fig. 3B, 3C), and similar results were obtained with patients with small nodules (< 3cm in diameter). And those with larger lung nodules (diameter > -3 cm) (fig. 3D).
Fig. 2(a, C) receiver operating characteristics of the performance of the model in the training queue (a) and the test queue (C). (B, D) Box plots represent the prognostic probability training group (B) and the test group (D) calculated by the model from the normal and malignant groups. P <0.05, P <0.01, P <0.001, P < 0.0001.
FIG. 3 is a block diagram showing the probability of prognosis calculated by the model from the test cohort. (cohort (a) prognostic probability levels of participants with different tumor stages · (stage · (B) prognostic probability levels of participants with and without metastasis · (((C) prognostic probability levels of different histological participants (D) prognostic probability levels of nodule size · magnitude of participants, × P <0.05, × P <0.01, × P <0.001, × P <0.0001).
The forum has demonstrated that some platelet characteristics are associated with lung cancer. The secretion of tumor-derived cytokines such as IL-1, GM-CSF, G-CSF, and IL-6 may affect the growth and differentiation of megakaryocytes. Megakaryocytes have a similar ability to secrete cytokines, which in turn can affect bone marrow endothelial cells to support megakaryocyte maturation. Based on this mechanism, lung cancer patients often have higher platelet counts than healthy people. As described in literature reviews, patients with lung cancer have been shown to suffer from thrombocythemia. Pedersen LM et al in 1996 reported that patients with pre-operative thrombocythemia had significantly higher frequency of thrombocythemia than the control, the subjects, and the survival rate was significantly reduced. Previous studies have also indicated that platelet count is an important prognostic factor for overall survival. According to previous studies, this study showed an increasing trend in platelet counts in patients with malignant tumors. Recently, many researchers have focused on the Mean Platelet Volume (MPV), which is associated with various malignancies, such as colon, gynecological and lung cancer. Several studies have shown that tumors can directly activate platelets in the blood or through secretion of activators. In addition to its carcinogenic effect, activated platelets also release vascular endothelial growth factor and activate the coagulation and fibrinolytic systems, thereby making the platelets more reactive. MPV is believed to reflect platelet activity, which demonstrates an increased value of MPV in cancer patients. In general, many published studies have shown that MPV values are elevated in lung cancer patients. Analysis by Abdullah Sakin indicates that MPV is an independent risk factor for OS [ OS [23 ]. On the other hand, there are still some clinical trials reporting a reduction in MPV associated with lung cancer. For example, in the Inagaki N' study, NSCLC patients had significantly lower MPV than healthy subjects [9.5] + -0.8, fl ]. However, our studies demonstrate a significant increase in the value of MPV in lung cancer. The Platelet Distribution Width (PDW) represents the change in platelet size. PDW will therefore be affected when the average platelet size increases. Previous observations support that PDW is independently correlated with the prognosis of lung cancer. In the Cui study, ROC analysis showed that if the cut-off point selected for PDW was 16.3, the specificity and sensitivity were 51.9% and 96.3%, respectively (AUC ═ 0.785, 95% CI: 0.732-0.833, P <0.0001). there was a direct relationship between PDW and MPV, and therefore, under physiological conditions, these two features generally changed in the same direction. In our results, PDW appeared to be indistinguishable between lung cancer patients and controls, but an upward trend was seen in lung cancer. Therefore, in future work, a large-scale investigation is required to confirm the association between an increase in PDW and lung cancer. After all, platelet count, MPV and PDW can be used as predictive factors for lung cancer in diagnostic models. Our findings are unexpected and indicate that platelet parameters in plasma are diagnostically comparable to those in whole blood. Surprisingly, despite extensive studies on PRP, platelets in PRP samples have never been reported. PRP contains large amounts of platelets, proteins and growth factors, and can enhance wound healing, induce angiogenesis and tissue regeneration. In particular, PRP contains high levels of TGF- β 1 (which has been shown to be an important factor in tumor progression) and metastasis. Therefore, we hypothesized that platelets in PRP samples may be associated with lung cancer. As expected, the value of the malignant group plt was significantly reduced, as was the value of pMPV. PRRs have also been proposed for the first time, and may be potential diagnostic markers. The possible explanation for these results may be due to the preparation method of PRP. In the malignant blood circulation, tumor cells activate and aggregate platelets, which in turn aggregate red blood cells. Centrifugation sedimentes out larger aggregated platelets, which results in consumption of platelets and leaves smaller platelets in the plasma. These results appear to be consistent with the results for the whole blood samples.
Even though the platelet characteristics of lung cancer patients differ from those of normal controls, the diagnostic efficiency of any individual feature is unsatisfactory. Thus, multivariate models for diagnosing lung cancer were developed. In the test cohort with five parameters (bPLT, bMPV, pPLT, pMPV, PRR), the performance of the model reached a high AUC of 0.77. Recent attention has focused on the diagnostic potential of platelets in whole blood samples, and a large body of literature has investigated only a single feature in diagnosis. The findings reported here provide a new idea for the diagnostic potential of the platelet in PRP samples, and the paper provides a deeper insight for the diagnosis of cancer in combination with multiple features. Platelet characterization was obtained without radioactivity and invasiveness as compared to imaging, pathology examination. Therefore, diagnostic models developed from platelet characteristics may have great application prospects.
Conclusion
Due to the limitations of the patient population, further studies on a larger patient population are recommended. In addition, the multicenter random control experiment can provide more definitive evidence. The results of this study can be used to develop targeted interventions to address the potential role of platelets in PRP samples.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (5)
1. A lung cancer model established by integrating platelet characteristics in blood and platelet rich plasma comprising the steps of:
s01, collecting whole blood samples of all participants, and randomly grouping the whole blood samples, wherein the groups comprise a training group and a testing group;
s02, measuring the platelet count, the average platelet volume, the platelet distribution width and the platelet volume by a blood cell analyzer;
s02, centrifuging to obtain platelet-rich plasma in the residual blood;
s03, measuring the platelet count, the average platelet volume, the platelet distribution width and the platelet volume in the platelet-rich plasma;
s04, carrying out variable screening on the data of the training set through a rank sum test pair;
s05, establishing a regression development diagnosis model through the variables with the differences in the training group and the variables reported in the literature;
s06, analyzing characteristic (ROC) curves in the training set and the testing set;
S07R by Nagelkerke2The performance of a regression development diagnosis model is evaluated by statistical data and quantitative discrimination;
s08, evaluating the sensitivity, the specificity and the corresponding 95% confidence interval of the training group, and calculating the cancer probability of all the subjects in the group;
s10, repeating the steps S06-S08 in the test group to evaluate the generalization performance of the model to the test group.
2. The method for modeling lung cancer according to platelet characteristics in integrated blood and platelet rich plasma according to claim 1, wherein the platelet count, mean platelet volume, platelet distribution width and platelet volume are determined by Mindray Coulter counter 6600 in step S02.
3. The method for modeling lung cancer according to the platelet characteristics in integrated blood and platelet rich plasma of claim 2, wherein the platelet count, the mean platelet volume, the width of the platelet distribution and the platelet volume in platelet rich plasma are determined by the Mindray Coulter counter 6600 in step S03.
4. The method for modeling lung cancer by integrating platelet characteristics in blood and platelet rich plasma according to claim 3 wherein the regression developed diagnostic model in step S05 is a logistic regression model with the formula: logit (p) ═ 16.30380+ coefficient of platelet count × coefficient of platelet count + average platelet volume × average platelet volume-coefficient of platelet count in platelet-rich plasma × coefficient of platelet count-average platelet volume in platelet-rich plasma × coefficient of average platelet volume in platelet-rich plasma + coefficient of platelet recovery × platelet recovery rate; all coefficients were obtained by logistic regression.
5. Modeling lung cancer according to platelet characteristics in integrated blood and platelet-rich plasma as claimed in claim 1, wherein R of Nagelkerke in step S082For quantifying the predictive strength of regression developed diagnostic models.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113288110A (en) * | 2021-04-23 | 2021-08-24 | 四川省肿瘤医院 | Model and system for predicting benign and malignant pulmonary nodules based on platelet parameters |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160292379A1 (en) * | 2013-11-07 | 2016-10-06 | Medial Research Ltd. | Methods and systems of evaluating a risk of lung cancer |
| CN106943192A (en) * | 2017-03-14 | 2017-07-14 | 上海交通大学医学院附属第九人民医院 | The method for building up of the preoperative forecast model of the expression index of lung carcinoma cell KI 67 |
| WO2018021153A1 (en) * | 2016-07-25 | 2018-02-01 | 国立大学法人信州大学 | Method of evaluating lung cancer risk state, lung cancer risk state evaluation device, lung cancer risk state evaluation program, lung cancer risk state evaluation system and information communication terminal device |
| WO2018187624A1 (en) * | 2017-04-06 | 2018-10-11 | The United States Government As Represented By The Department Of Veterans Affairs | Methods of detecting lung cancer |
| CN109884302A (en) * | 2019-03-14 | 2019-06-14 | 北京博远精准医疗科技有限公司 | Lung cancer early diagnosis marker and its application based on metabolism group and artificial intelligence technology |
-
2020
- 2020-06-24 CN CN202010595203.5A patent/CN111933287A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160292379A1 (en) * | 2013-11-07 | 2016-10-06 | Medial Research Ltd. | Methods and systems of evaluating a risk of lung cancer |
| WO2018021153A1 (en) * | 2016-07-25 | 2018-02-01 | 国立大学法人信州大学 | Method of evaluating lung cancer risk state, lung cancer risk state evaluation device, lung cancer risk state evaluation program, lung cancer risk state evaluation system and information communication terminal device |
| CN106943192A (en) * | 2017-03-14 | 2017-07-14 | 上海交通大学医学院附属第九人民医院 | The method for building up of the preoperative forecast model of the expression index of lung carcinoma cell KI 67 |
| WO2018187624A1 (en) * | 2017-04-06 | 2018-10-11 | The United States Government As Represented By The Department Of Veterans Affairs | Methods of detecting lung cancer |
| CN109884302A (en) * | 2019-03-14 | 2019-06-14 | 北京博远精准医疗科技有限公司 | Lung cancer early diagnosis marker and its application based on metabolism group and artificial intelligence technology |
Non-Patent Citations (1)
| Title |
|---|
| RUILING ZU 等: "Integration of platelet features in blood and platelet rich plasma for detection of lung cancer", 《CLINICA CHIMICA ACTA》, pages 2 - 4 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113288110A (en) * | 2021-04-23 | 2021-08-24 | 四川省肿瘤医院 | Model and system for predicting benign and malignant pulmonary nodules based on platelet parameters |
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