CN113782124A - Establishment and improvement of pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell depletion - Google Patents
Establishment and improvement of pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell depletion Download PDFInfo
- Publication number
- CN113782124A CN113782124A CN202111079561.1A CN202111079561A CN113782124A CN 113782124 A CN113782124 A CN 113782124A CN 202111079561 A CN202111079561 A CN 202111079561A CN 113782124 A CN113782124 A CN 113782124A
- Authority
- CN
- China
- Prior art keywords
- prediction
- prediction model
- tumor cell
- suidan
- ovarian cancer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 210000004881 tumor cell Anatomy 0.000 title claims abstract description 41
- 206010033128 Ovarian cancer Diseases 0.000 title claims abstract description 30
- 206010061535 Ovarian neoplasm Diseases 0.000 title claims abstract description 25
- 238000011156 evaluation Methods 0.000 title claims description 10
- 230000006872 improvement Effects 0.000 title claims description 7
- 206010028980 Neoplasm Diseases 0.000 claims abstract description 18
- 230000008033 biological extinction Effects 0.000 claims abstract description 7
- 230000035945 sensitivity Effects 0.000 claims abstract description 7
- 238000012360 testing method Methods 0.000 claims abstract description 4
- 238000012274 Preoperative evaluation Methods 0.000 claims abstract 2
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000003902 lesion Effects 0.000 claims description 2
- 231100000915 pathological change Toxicity 0.000 claims 2
- 230000036285 pathological change Effects 0.000 claims 2
- 210000000952 spleen Anatomy 0.000 claims 2
- 206010000050 Abdominal adhesions Diseases 0.000 claims 1
- 206010003445 Ascites Diseases 0.000 claims 1
- 101100027969 Caenorhabditis elegans old-1 gene Proteins 0.000 claims 1
- 208000019428 Ligament disease Diseases 0.000 claims 1
- 208000007433 Lymphatic Metastasis Diseases 0.000 claims 1
- 210000000232 gallbladder Anatomy 0.000 claims 1
- 210000003041 ligament Anatomy 0.000 claims 1
- 210000004185 liver Anatomy 0.000 claims 1
- 210000003975 mesenteric artery Anatomy 0.000 claims 1
- 210000002796 renal vein Anatomy 0.000 claims 1
- 230000003307 reticuloendothelial effect Effects 0.000 claims 1
- 230000008719 thickening Effects 0.000 claims 1
- 230000009467 reduction Effects 0.000 abstract description 11
- 208000007571 Ovarian Epithelial Carcinoma Diseases 0.000 description 5
- 238000002591 computed tomography Methods 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 4
- 210000002966 serum Anatomy 0.000 description 4
- 238000013170 computed tomography imaging Methods 0.000 description 3
- 230000002435 cytoreductive effect Effects 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000004083 survival effect Effects 0.000 description 3
- 238000011226 adjuvant chemotherapy Methods 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 230000036210 malignancy Effects 0.000 description 2
- 230000002611 ovarian Effects 0.000 description 2
- 206010059866 Drug resistance Diseases 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 102000040739 Secretory proteins Human genes 0.000 description 1
- 108091058545 Secretory proteins Proteins 0.000 description 1
- 239000000427 antigen Substances 0.000 description 1
- 102000036639 antigens Human genes 0.000 description 1
- 108091007433 antigens Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000034994 death Effects 0.000 description 1
- 231100000517 death Toxicity 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000000918 epididymis Anatomy 0.000 description 1
- 201000010063 epididymitis Diseases 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 210000004996 female reproductive system Anatomy 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 238000011221 initial treatment Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000000034 method Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000405 serological effect Effects 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000011282 treatment Methods 0.000 description 1
- 239000000439 tumor marker Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
Establishing and improving a pre-operative evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell extinction, performing improved modeling on the basis of a Sduidan score scale, adding serology HE4 and ROMA indexes, giving prediction scores of 1 score respectively according to calculated HE4 & gt 264.7pmol/L, CA125 & gt 545.6U/ml and ROMA indexes & gt 90.0% boundary values in data, and calculating the prediction scores of all cases by two different radiology senior physicians for secondary scoring; finally, drawing an area AUC under a test subject working curve obtained by an ROC curve according to the prediction score and the operation result, and evaluating the clinical value of the model for predicting the unsatisfactory tumor cell debulking of the patient with the advanced ovarian cancer; the prediction model of the invention has higher prediction value than the Suidan prediction model, the sensitivity of the prediction model for unsatisfied tumor cell reduction is obviously higher than the Suidan scoring scale, and the prediction model provides a certain prediction value basis for the clinician to judge whether the satisfactory tumor reduction can be performed.
Description
Technical Field
The invention belongs to the technical field of medicine, and particularly relates to establishment and improvement of a pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell debulking.
Background
Epithelial ovarian malignancies are the most mortality tumors of the female reproductive system. Epidemiological data indicate that epithelial ovarian cancer accounts for 52% of deaths caused by gynecological malignancies. 70% of patients are found to be already advanced, when extensive pelvic peritoneal spreading has already occurred, with a 5-year survival rate of advanced ovarian cancer of only 20-30%. 2020NCCN (American national comprehensive cancer System) guidelines recommend that the overall survival rate and the tumor-free survival rate of patients can be increased if an initial satisfactory tumor cell debulking operation without macroscopic residual lesions is carried out on advanced epithelial ovarian cancer, and the guidelines recommend that an intermediate tumor cell debulking operation is carried out after a new adjuvant chemotherapy on patients who cannot carry out the initial satisfactory tumor cell debulking operation, but how to carry out an operation during the initial diagnosis of the ovarian cancer patients or the new adjuvant chemotherapy is accurate, noninvasive and economic prediction and diversion, so that the curative effect of the patients is improved, and the occurrence of secondary operations or drug resistance of the patients is avoided, which is a problem that gynecologists need to solve urgently.
Aiming at the clinical problem, Suidan et al in 2014 performed prospective research on the value of prediction of satisfactory tumor reduction and extinction by combining preoperative imaging index CT with serum marker CA125 (tumor marker antigen 125), selects 3 clinical indexes and 8 imaging indexes, forms a CT (computed tomography) scoring model to predict that the proportion of unsatisfactory tumor reduction is obviously increased when the CT scoring result is more than 4, but the model serological index only has CA125, and the level of the serum CA125 is influenced by various diseases such as inflammation, whether to combine with different diseases and other factors, can not well reflect the real situation of the ovarian cancer disease and has certain limitation; HE4 (human epididymis secretory protein 4) is widely applied to diagnosis and follow-up of epithelial ovarian cancer at present, results of European clinical research project OVCAD (ovarian cancer diagnosis Association) suggest that serum HE4 can be used as a relevant index for predicting satisfactory tumor cytoreduction and has a prediction value superior to that of serum CA125, and ROMA (ovarian cancer malignant tumor risk algorithm) index is a prediction index calculated by combining HE4, CA125 and modeling on the basis of the menopausal state of a patient, and ROMA calculated by combining CA125 and HE4 can reflect tumor load more accurately, but the critical value of the ROMA is not judged by unified standard at present.
Disclosure of Invention
Aiming at the defects existing in the establishment of the existing evaluation model for the satisfactory tumor reduction and extinction of the late ovarian cancer, the invention provides an improved model, by researching the prediction value of serology CA125, HE4 and ROMA index levels on the satisfactory tumor reduction and extinction, HE4 and ROMA indexes are brought into the prediction model, a new ROMA comprehensive prediction ovarian cancer satisfactory tumor cell reduction and extinction score table is established by combining CT imaging indexes, the prediction value of the table is detected by comparing with the original Suidan score table, and theoretical support is provided for establishing a reasonable and effective initial treatment scheme for patients with the late ovarian cancer.
In order to achieve the purpose, the invention provides the following technical scheme:
establishment and improvement of a pre-operation evaluation and prediction model for advanced ovarian cancer satisfactory tumor cell reduction. Carrying out improved modeling on the basis of a Sduidan score scale, adding serology HE4 and ROMA indexes, assigning prediction scores of 1 score respectively according to HE4 & gt 264.7pmol/L, CA125 & gt 545.6U/ml and ROMA indexes & gt 90.0% boundary values obtained by calculation in data, and giving secondary scoring to different two high-age radiologists to calculate the prediction scores of all cases; and finally, drawing an area AUC under a test subject working curve obtained by an ROC curve according to the prediction score and the operation result, and evaluating the clinical value of the model for predicting the dissatisfaction of the advanced ovarian cancer patient on tumor cell debulking.
By drawing a working curve ROC of a subject, the value of the improved prediction model and the Sduidan score scale in the unsatisfied tumor cell debulking prediction is compared, the result shows that in the comparison of the area AUC under the curve, the score scale (0.896) is higher than the Suidan score (0.881), which indicates that the improved prediction model has higher prediction value, the sensitivity of the improved prediction model in the unsatisfied tumor cell debulking prediction is obviously higher than that of the Suidan score scale, which indicates that the possibility that the patient cannot complete the satisfactory tumor cell debulking prediction before the evaluation of the ovarian cancer satisfactory tumor cell debulking and the prediction score scale judge that the patient cannot complete the satisfactory tumor cell debulking prediction before the operation is higher, and a basis with a certain prediction value is provided for a clinician to judge whether the satisfactory tumor debulking can be performed.
On the basis of a Suidan model combining early-stage imaging with serology CA125, a comprehensive establishment mode combining HE4 and ROMA indexes with CT imaging indexes is introduced, the clinical value of the mode for predicting satisfactory tumor cytoreduction during the primary operation of ovarian cancer is discussed, and the predicted value of HE4 critical value of 264.7pmol/L and ROMA >90.0 percent is determined through an ROC (receiver operating curve) curve, and the sensitivity and specificity of the predicted value on the recurrence and the operative thoroughness of the advanced epithelial ovarian cancer are determined.
TABLE 2 comparison of the value of the present assessment and Pre-measurement Table with Suidan score to predict satisfactory tumor cell debulking
Table 2), demonstrating that primary tumor cell debulking was achieved when the improved predictive model score was less than 4 min
TABLE 3 comparison of the improved rating Scale of the present invention with the predicted compliance rate of Suidan scores
The likelihood of satisfaction is greater, while the likelihood of tumor cell debulking being satisfactory is greater when the Suidan score cut-off is less than 3 minutes.
Compared with the prior art, the invention has the beneficial effects that:
(1) the boundary between the present invention and Suidan prediction model was calculated by ROC curve, and the boundary on the modified score scale was 4 points, indicating that when the modified prediction model score was less than 4 points, the primary tumor cell debulking was more likely to be satisfactory, and when the Suidan score boundary was less than 3 points, the tumor cell debulking was more likely to be satisfactory (see Table 2).
(2) Comparing the coincidence rates of the present invention and the Suidan prediction model in practical application processes, based on the cutoff values shown in table 3, we found that the improved pre-measurement table not only predicted a slightly higher coincidence rate than Suidan score in satisfactory tumor reduction, but also predicted a higher coincidence rate in unsatisfactory tumor cell reduction.
(3) Based on the research that HE4 has higher prediction value than CA125 in the primary satisfactory tumor cell debulking of epithelial ovarian cancer and the ROMA index calculated by combining the value of the HE4 with that of the CA125 and the HE4, the ROMA is integrated with CT imaging indexes to form a score scale, whether the epithelial ovarian patient can undergo the primary tumor cell debulking is predicted by applying the score scale and the Suidan score scale, and the result shows that the accuracy of the unsatisfied tumor cell debulking prediction is remarkably improved by the improved scale.
(4) The invention adopts a non-invasive means, obviously improves the accuracy of predicting the first satisfactory tumor cytoreduction operation on the basis of not increasing the economic burden of the patient, can provide more accurate and effective guidance for the selection of the treatment scheme of the patient during the initial diagnosis, and benefits the patient to the greatest extent.
Drawings
FIG. 1 is a schematic representation of ROC curves for the present invention and Suidan prediction model to predict satisfactory tumor cell debulking.
Detailed Description
The embodiments of the present invention will be described with reference to the drawings, tables 1, 2 and 3.
Establishment and improvement of a pre-operation evaluation and prediction model for advanced ovarian cancer satisfactory tumor cell reduction. Carrying out improved modeling on the basis of a Sduidan score scale, adding serology HE4 and ROMA indexes, assigning prediction scores to be respectively 1 score according to HE4 & gt 264.7pmol/L, CA125 & gt 545.6U/ml and ROMA index & gt 90.0% threshold values obtained by calculation in data (see table 1), and carrying out secondary scoring by two different radiology senior physicians to calculate the prediction scores of all cases; and finally, drawing an area AUC under a test subject working curve obtained by an ROC curve according to the prediction score and the operation result, and evaluating the clinical value of the model for predicting the dissatisfaction of the advanced ovarian cancer patient on tumor cell debulking.
By plotting the receiver operating curve ROC, the value of the improved predictive model of the invention compared to the sdidan scale in predicting unsatisfactory tumor cell debulking is shown in the area under the curve AUC comparison, fig. 1 and predictive measurements table 2 show:
sensitivity: 80% of Suidan prediction model and 86.67% of prediction model;
specificity: 80.65% of Suidan prediction model and 74.19% of prediction model of the invention;
accuracy: suidan prediction model 0.6005, prediction model 0.6086 of the present invention;
AUC: suidan prediction model 0.881, prediction model 0.896 of the present invention;
boundary value: the Suidan prediction model is divided into 3 points, and the prediction model of the invention is divided into 4 points.
The prediction value of the prediction model is higher than that of a Suidan prediction model, the sensitivity of the prediction model for unsatisfied tumor cell debulking is obviously higher than that of a Suidan score scale, so that the probability that the patient cannot complete the satisfactory tumor cell debulking before the evaluation and the prediction score scale for the satisfactory tumor cell debulking of the ovarian cancer is higher, and a basis with a certain prediction value is provided for a clinician to judge whether the satisfactory tumor debulking can be performed.
Claims (2)
1. Establishing and improving a pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell extinction, which is characterized in that improvement modeling is carried out on the basis of a Sduidan score scale, serology HE4 and ROMA indexes are added, prediction scores are respectively given as 1 score according to HE4 > 264.7 pmol/63125 > 545.6U/ml and ROMA index > 90.0% boundary values obtained by calculation in data, and two different radiology high-age physicians are requested to carry out secondary scoring to calculate the prediction scores of all cases; finally, drawing an area AUC under a test subject working curve obtained by an ROC curve according to the prediction score and the operation result, and evaluating the clinical value of the model for predicting the unsatisfactory tumor cell debulking of the patient with the advanced ovarian cancer; the assessment and prediction scoring scale before extinction was as follows:
TABLE 1 Pre-evaluation and predictive Scoring Scale for satisfactory tumor cell debulking in advanced ovarian cancer
。
2. Establishment and improvement of the pre-operative evaluation and prediction model for satisfactory tumor cell debulking of advanced ovarian cancer according to claim 1, characterized by comparing the value of the improved prediction model of the invention with the Sduidan score scale in the prediction of unsatisfactory tumor cell debulking by plotting the subject working curve, the results show that:
sensitivity: 80% of Suidan prediction model and 86.67% of prediction model;
specificity: 80.65% of Suidan prediction model and 74.19% of prediction model of the invention;
accuracy: suidan prediction model 0.6005, prediction model 0.6086 of the present invention;
AUC: suidan prediction model 0.881, prediction model 0.896 of the present invention;
boundary value: 3 points of Suidan prediction model and 4 points of prediction model of the invention;
the prediction value of the prediction model is higher than that of a Suidan prediction model, the sensitivity of the prediction model for unsatisfied tumor cell debulking is obviously higher than that of a Suidan score scale, so that the probability that the patient cannot complete the satisfactory tumor cell debulking before the evaluation and the prediction score scale for the satisfactory tumor cell debulking of the ovarian cancer is higher, and a basis with a certain prediction value is provided for a clinician to judge whether the satisfactory tumor debulking can be performed.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111079561.1A CN113782124A (en) | 2021-09-15 | 2021-09-15 | Establishment and improvement of pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell depletion |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111079561.1A CN113782124A (en) | 2021-09-15 | 2021-09-15 | Establishment and improvement of pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell depletion |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113782124A true CN113782124A (en) | 2021-12-10 |
Family
ID=78844326
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111079561.1A Pending CN113782124A (en) | 2021-09-15 | 2021-09-15 | Establishment and improvement of pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell depletion |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113782124A (en) |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016198749A1 (en) * | 2015-06-12 | 2016-12-15 | Turun Yliopisto | Diagnostic biomarkers, clinical variables, and techniques for selecting and using them |
CN110913890A (en) * | 2017-05-16 | 2020-03-24 | 国家医疗保健研究所 | Methods and pharmaceutical compositions for treating acute ischemic stroke |
CN113325178A (en) * | 2021-05-27 | 2021-08-31 | 江苏省肿瘤医院 | Detection kit for early diagnosis and prognosis evaluation of ovarian cancer |
-
2021
- 2021-09-15 CN CN202111079561.1A patent/CN113782124A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016198749A1 (en) * | 2015-06-12 | 2016-12-15 | Turun Yliopisto | Diagnostic biomarkers, clinical variables, and techniques for selecting and using them |
CN110913890A (en) * | 2017-05-16 | 2020-03-24 | 国家医疗保健研究所 | Methods and pharmaceutical compositions for treating acute ischemic stroke |
CN113325178A (en) * | 2021-05-27 | 2021-08-31 | 江苏省肿瘤医院 | Detection kit for early diagnosis and prognosis evaluation of ovarian cancer |
Non-Patent Citations (5)
Title |
---|
ARPITA SURI 等: "Diagnostic measures comparison for ovarian malignancy risk in Epithelial ovarian cancer patients: a meta-analysis", SCIENTIFIC REPORTS, 27 August 2021 (2021-08-27), pages 1 - 14 * |
袁美莉: "HE4与CA125联合检测在卵巢癌诊断中的应用价值", 中国优秀硕士学位论文全文数据库 医药卫生科技辑, no. 08, 15 August 2016 (2016-08-15), pages 072 - 485 * |
钱自强 等: "血清CA125、HE4及ROMA在卵巢肿瘤的良恶性诊断中的应用价值", 医学信息, vol. 33, no. 04, 15 February 2020 (2020-02-15), pages 175 - 176 * |
陈玲: "血清CA125、HE4及ROMA指数联合CT评分预测晚期上皮性卵巢癌初始手术结局的临床价值", 中国优秀硕士学位论文全文数据库 医药卫生科技辑, no. 03, 15 March 2021 (2021-03-15), pages 068 - 18 * |
马少寒: "改良卵巢癌评分量表预测晚期上皮性卵巢癌满意减瘤手术的临床价值", 中国优秀硕士学位论文全文数据库 医药卫生科技辑, no. 02, 15 February 2022 (2022-02-15), pages 068 - 35 * |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Omer et al. | The diagnostic role of human epididymis protein 4 and serum amyloid-A in early-stage endometrial cancer patients | |
Huang et al. | Diagnostic value of HE4 in ovarian cancer: a meta-analysis | |
Chen et al. | HE4 as a serum biomarker for ROMA prediction and prognosis of epithelial ovarian cancer | |
Kawaguchi et al. | Posttreatment cut-off levels of squamous cell carcinoma antigen as a prognostic factor in patients with locally advanced cervical cancer treated with radiotherapy | |
Sun et al. | The volume-metabolic combined parameters from 18F-FDG PET/CT may help predict the outcomes of cervical carcinoma | |
Qian et al. | Feasibility study on predicting recurrence risk of bladder cancer based on radiomics features of multiphase CT images | |
Zheng et al. | Combination analysis of a radiomics-based predictive model with clinical indicators for the preoperative assessment of histological grade in endometrial carcinoma | |
Cui et al. | Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics | |
Liu et al. | Meta-analysis of the diagnostic accuracy of HE4 for endometrial carcinoma | |
Zhang et al. | Multimodal MRI-based radiomics-clinical model for preoperatively differentiating concurrent endometrial carcinoma from atypical endometrial hyperplasia | |
Liu et al. | Multi-parameter MR radiomics based model to predict 5-year progression-free survival in endometrial cancer | |
Qi et al. | A study on the clinical value of 18 F‑fluorodeoxyglucose positron emission tomography/computed tomography combined with serum squamous cell carcinoma antigen in diagnosing recurrence/metastases in patients with early metaphase cervical cancer | |
CN113782124A (en) | Establishment and improvement of pre-operation evaluation and prediction model for late stage ovarian cancer satisfactory tumor cell depletion | |
Xie et al. | Nomogram for preoperative estimation of orbit invasion risk in periocular squamous cell carcinoma | |
Yuan et al. | An MRI-based pelvimetry nomogram for predicting surgical difficulty of transabdominal resection in patients with middle and low rectal cancer | |
Al‐Idrissi et al. | Breast cancer in a low‐risk population. The influence of age and menstrual status on disease pattern and survival in Saudi Arabia | |
Demirtas et al. | Proposal and validation of a novel scoring system for hepatocellular carcinomas beyond curability borders | |
McGuckin et al. | Circulating tumour-associated mucin concentrations, determined by the CASA assay, in healthy women | |
Qiao et al. | External validation and improvement of the scoring system for predicting the prognosis in hepatocellular carcinoma after interventional therapy | |
Cai et al. | Use of a novel index, the A-index, and its associated nomogram to predict overall survival rates after resection of primary hepatocellular carcinoma | |
US11605460B2 (en) | Multi-targeted fibrosis tests | |
Shi et al. | Assessment of serum tumor markers for predicting ocular metastasis in lung adenocarcinoma: a retrospective study | |
Kong et al. | Clinical value of ultrasound parameters PI, TTP, and MTT in assessing cervical lymph node metastasis of papillary thyroid carcinoma | |
Liu et al. | Identifying the risk factors and developing a predictive model for postoperative pelvic floor dysfunction in cervical cancer patients | |
Huang et al. | Comparison of Clinical Staging and Secondary Staging of Cervical Cancer |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |