CN110047576A - A kind of standardization lipiodol deposition classification method based on medical image - Google Patents
A kind of standardization lipiodol deposition classification method based on medical image Download PDFInfo
- Publication number
- CN110047576A CN110047576A CN201910336876.6A CN201910336876A CN110047576A CN 110047576 A CN110047576 A CN 110047576A CN 201910336876 A CN201910336876 A CN 201910336876A CN 110047576 A CN110047576 A CN 110047576A
- Authority
- CN
- China
- Prior art keywords
- lipiodol
- deposition
- classification method
- standardization
- classification
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- 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
- G16H30/00—ICT specially adapted for the handling or processing of medical images
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Radiology & Medical Imaging (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention discloses a kind of, and the standardization lipiodol based on medical image deposits classification method, include: that segmentation threshold is reformulated to each existing lipiodol deposition classification schemes, constructs a kind of exponential type lipiodol using the classification method obtained according to new segmentation threshold as independent variable and deposit sorting algorithm and quantitatively divide to being deposited into professional etiquette generalized based on the lipiodol on medical image.The standardization lipiodol deposition classification method based on medical image of the invention, which can deposit the lipiodol on the medical image after hepatic arterial chemoembolization, carry out classification and go forward side by side the quantitative division of professional etiquette generalized.
Description
Technical field
The invention belongs to technical field of medical image processing, and in particular to a kind of standardization lipiodol based on medical image is heavy
Product classification method.
Background technique
TACE postoperative lipiodol deposition Classification and Identification is hot topic class in Medical Image Processing and application field research in recent years
One of topic and a complexity and significant research work.Transcatheter arterial chemoembolization (TACE) is that known be directed to cannot
Perform the operation excision mid and late liver cancer critical treatment means, in the interventional procedure of liver cancer using lipiodol as carrier with
Anticancer drug is mixed into emulsion and carries out embolism to tumour, there is a double action of sustained-release chemotherapy drug and embolism, and the postoperative iodine of TACE
The form and curative effect of oil deposition are highly relevant, and lipiodol deposition data assist in doctor and make correct diagnosis, formulate treatment
Scheme and observation curative effect.
It is deposited both at home and abroad for lipiodol after transcatheter arterial chemoembolization at present, mainly there is following four classes method:
One, using lipiodol deposition ratio as the classification method of foundation.This method is four classification methods based on depositional configuration, is based on iodine
Lipiodol deposition is divided into following classification: type I by the ratio of oil deposition, the classification method: lipiodol deposits completely namely 100% is heavy
Product；Type II: defective lipiodol deposition, deposition ratio is about > 75%；Type-iii: inhomogeneous lipiodol deposition deposits ratio
It is about > 20%；Type IV: slight lipiodol deposition, lipiodol at this time are deposited as about 20% or less.And existing research person proposes: class
Add up within type I patient 1 year to be about about 85% without recurrence rate, adds up without recurrence rate to be about 50% within Type II patient 1 year, type I suffers from
1 year overall survival of person is about 90%, and the recurrence rate after type I patient's four weeks is probably 38%, and Type II is about 57%, and type IV is
100%。
Two, using necrotic zone as the classification method of foundation.This method, which deposits lipiodol, classifies are as follows: higher than 50% and is lower than
50%.According to existing data, for necrotic zone, the complete incidence graph of pathology level, part alleviate, stable disease with
Progression of disease is alleviated with the complete incidence graph of lipiodol deposition level, part, stable disease and progression of disease with significant are positively correlated
Property (66.7%, 60.9%, 56.5%, 70.0%).
Three, using lipiodol depositional model as the classification method of foundation.This method usually by lipiodol deposition be divided into completely deposition with
Not exclusively deposition two major classes or compact deposits and two classes of un-densified deposition.Wherein, now studies have pointed out that lipiodol deposits completely
It is corresponding good or pathology necrosis as a result, necrosis is about 89%, and then to correspond to undesirable pathology bad for incomplete lipiodol deposition
Extremely, necrosis is about < 43%.With the patient deposited completely, median survival time is about 50 months, but for not exclusively depositing
Patient, median survival time are about 30 months.For depositing patient completely, one year, 2 years survival rates are about 92.7% and
70.7%, for not exclusively depositing patient, one year, 2 years survival rates are about 60.8% and 28.0%.When lipiodol is deposited as clearly
It can be seen that and when being evenly distributed on inside tumor, lipiodol deposition is considered as complete or fine and close depositional model.Existing research
It points out, the survival rate of fine and close lipiodol deposition patient is significantly larger than the patient of un-densified deposition.
Four, using lipiodol deposition distribution as the classification method of foundation.This method is according to lipiodol deposition distribution, the i.e. absorption of lipiodol
TACE treatment results are carried out different stage classification by situation.Lipiodol is fixed completely and uniformly when one inside tumor absorbs
Justice is tubercle absorption mode, wherein nodular type deposition is fully retained and the uniform oncolipid intake of display strongly, and it is endless
The nodular type deposition of full retention is shown as not absorbing in tumor region.It diffuses distribution pattern and is defined as irregular lipiodol
Retention, wherein obscure boundary Chu, the distribution for being related to multiple segments are known as diffusing distributed mode with non-uniform distribution character
Formula；Multiple tubercles, which have the lipiodol retention of different size and degree and are related to most of liver, is then known as diffusing more knots under mode
Save lipiodol distribution.Previously research thinks that 1 year accumulative survival rate of the liver cancer patient of complete tubercle retention is up to 76%, not exclusively
1 year accumulative survival rate of liver cancer patient of nodular type retention be about 67%.Diffusivity heterogeneity distribution pattern is 32%, more tubercles
Property lipiodol distribution liver cancer patient uptake ratio be 46%.
Above-mentioned all kinds of lipiodol deposition methods can embody the lipiodol deposition after TACE is treated one month to a certain extent
With the relationship of long term survival prognosis, but reaction in a certain respect divides it after all only accounting for treatment, and actually iodine
Oily deposition is more complicated.
Have multinomial research at present to point out, on the basis of being main classification foundation with lipiodol deposition rate, it should by pathology
Necrosis, depositional model and deposition distribution account for range.In the big liver cancer of lipiodol accumulation some fine and close but annular in shape, tumour
Center be in downright bad shape.If lipiodol deposits ratio less than 75%, this kind of case can be classified as low/medium degree gather rather than
Completely/accumulation strongly；But clinically, inventors believe that these cases do not have residual tumor, the present inventor suggests using
When our classification, they are classified as completely/accumulation strongly.
The present inventor does not unite for the classification specification deposited both at home and abroad for lipiodol after transcatheter arterial chemoembolization at present
One status, in summary lipiodol deposits classification method, by deposition kenel, deposition ratio, pathology necrosis and deposition distribution situation
It takes into consideration, proposes a kind of complete standardization lipiodol deposition classification method based on medical image.
Summary of the invention
The object of the present invention is to provide a kind of, and the standardization lipiodol based on medical image deposits classification method, solves existing
The lipiodol deposition classification problem that situation is simple, error is big.
A kind of standardization lipiodol based on medical image of the present invention deposits classification method characterized by comprising
Deposit classification method according to the existing standardization lipiodol based on medical image: lipiodol deposits ratio, necrotic zone, lipiodol and deposits mould
Formula and lipiodol deposition distribution classification method redesign segmentation threshold to each subclass of each classification method, to each
Method formulates the subclass partition mode of fining, to constitute the lipiodol deposition classification matrix of standardization；And it is directed to the rule
Generalized lipiodol deposition classification matrix proposes a kind of exponent arithmetic model indicated by following formula, and to lipiodol deposition point
Class method carries out index budget, constructs and a kind of merges the above-mentioned existing standardization lipiodol deposition classification method based on medical image
Normalized quantized method:
Wherein,DrRatio classification method is deposited for lipiodol,CpFor lipiodol depositional model classification method,NcFor lipiodol necrotic zone point
Class method,UpFor lipiodol deposition distribution classification method, bTo reserve the factor.
The standardization lipiodol based on medical image deposits classification method, which is characterized in that for existing with lipiodol
Deposition ratio be classification foundation method, using necrotic zone after lipiodol as the method for classification foundation, with lipiodol depositional model be point
The method of class foundation, using lipiodol deposition distribution as the method for classification foundation, to each classification method weight in above-mentioned 4 kinds of methods
New design segmentation threshold, formulates unified subclass partition mode, constructs a kind of lipiodol deposition classification matrix of standardization.
The standardization lipiodol based on medical image deposits classification method, which is characterized in that heavy to the standardization
Integral matroid proposes a kind of index operation method, and carries out index budget to lipiodol deposition classification method, comprising: root
Classification matrix is deposited according to the standardization, using classification method each in matrix as independent variable, is drawn according to the subclass of the fining
Merotype constructs a kind of lipiodol deposition index model of fusion using each subclass of each method as independent variable value range.
The standardization lipiodol based on medical image deposits classification method, which is characterized in that the institute in above-mentioned formula
The segmentation threshold for stating reset is as follows: DrRatio classification method is deposited for lipiodol,Dr=0.00 indicates 100% lipiodol deposition,Dr
=1.00 indicate 85% to 99% lipiodol deposition,Dr=2.00 indicate 35% to 85% lipiodol deposition,Dr=3.00 indicate that < 35% lipiodol is heavy
Product；WithCpIndicate lipiodol deposition whether Zhi Mi situation,Cp=0.50 indicates dense form lipiodol deposition,Cp=1 indicates other types
Lipiodol deposition；WithNcIndicate necrotic zone,Nc=0 indicates 100% necrosis,Nc=0.50 indicates 50% to 99% necrosis,Nc=1 generation
The other necrosis of table；WithUpIndicate deposition distribution,Up=0.33 indicates tubercle deposition distribution mode,Up=0.50 indicates to diffuse mode
More tubercle lipiodol deposition distribution modes,Up=1.00 indicate other modes.
Beneficial effects of the present invention: the standardization lipiodol of the invention based on medical image deposits classification method, is based on institute
Lipiodol deposition index model is stated, the postoperative lipiodol of patients with hepatocellular carcinoma can be deposited and accurately be divided, professional etiquette of going forward side by side model
That changes quantifies.
Detailed description of the invention
Fig. 1 is the schematic diagram of the standardization lipiodol deposition classification method based on medical image.
Fig. 2 is constructed based on necrotic zone and lipiodol deposition distribution after lipiodol deposition ratio, lipiodol depositional model, lipiodol
The schematic diagram of standardization lipiodol deposition classification method based on medical image.
Fig. 3 is figure of the standardization lipiodol deposition index operational model in the verification result of 1302 patients, wherein
S101 is first step, S102 is second step, L1 is Band 1, L2 is the second group, L3 is third group, LD is lipiodol
Deposition,DrFor deposition ratio,NcFor necrotic zone.
Specific embodiment
Classification method is deposited below with reference to the accompanying drawings to describe the standardization lipiodol of the present invention based on medical image
Embodiment.It will also be recognized by those skilled in the art that without departing from the spirit and scope of the present invention, Ke Yiyong
A variety of different modes or combinations thereof are modified described embodiment.Therefore, attached drawing and description are inherently explanation
Property, it is not intended to limit the scope of the claims.
Fig. 1 is the schematic diagram for showing the standardization lipiodol deposition classification method based on medical image of the embodiment of the present invention.
As shown in Figure 1, the standardization lipiodol deposition classification method based on medical image includes: S101, deposited according to existing lipiodol
Splitting method: lipiodol deposits ratio, necrotic zone, lipiodol depositional model, lipiodol deposition distribution, resets to each method
Segmentation threshold carries out subclass division, specifically, withDrIndicate lipiodol deposition ratio, withNcIndicate necrotic area, withCpIndicate lipiodol
Depositional model, withUpIndicate lipiodol deposition distribution, and result corresponding to each subclass to each classification method carries out square
Matrix representation, to constitute standardization deposition classification matrix；S102 deposits classification type, construction according to the lipiodol after the conclusion
It is quantitative that a kind of exponential type lipiodol deposition sorting algorithm is deposited into professional etiquette generalized to the lipiodol based on medical image.
It specifically, include: for existing in the standardization lipiodol deposition classification method based on medical image in S101
Have and is deposited as the method for classification foundation, using necrotic zone after lipiodol as the method for classification foundation, with lipiodol using lipiodol deposition ratio
Mode is the method for classification foundation, using lipiodol deposition distribution as the method for classification foundation, to each height of each classification method
The design of result corresponding to class redesigns segmentation threshold, formulates the subclass partition mode of optimization, constructs a kind of sinking for standardization
Integrate matroid.
Specifically, in S102, a kind of index operation method is proposed to standardization deposition classification matrix, and to above
Method carries out index budget, comprising: classification matrix is deposited according to the standardization, using classification method each in matrix as change certainly
Amount carries out subclass division according to the segmentation threshold reformulated, using each subclass of each method as independent variable value model
It encloses, constructs a kind of lipiodol deposition index model of fusion.
In application, the center of tumour is in bad due in the case where the big liver cancer of lipiodol accumulation some fine and close but annular in shape
Death situation, and when lipiodol deposition ratio is less than 75%, this kind of case is classified as low/medium degree accumulation rather than complete/strong product
It is poly-, however, in the present invention, being classified as complete/strong because clinically thinking that these cases do not have residual tumor
Strong accumulation, and combine lipiodol deposition ratio, necrotic zone, the classification method of lipiodol depositional model and lipiodol deposition distribution and its
Prognosis situation carries out classification division, to form lipiodol deposition classification matrix, the results are shown in Table 1.
In table 1, CNU is the complete deposition under tubercle absorption mode, and INU is the incomplete deposition under tubercle absorption mode, DHU
For the uneven lipid intake for diffusing mode, MU is the more tubercles absorption for diffusing mode.In table 1, system that each classification includes
Counting word is reference with the existing statistical data analysis for delivering research data, the study found that indicating that when being in same a line
There is stronger correlation between this.
In conclusion the classification schemes of the classification thresholds according to the reset carry out exponential type mathematics model analysis,
To which intuitive reaction lipiodol deposits Clinical Outcome corresponding to different situations comprehensively, the lipiodol deposition index model is by following
Formula indicates:
Wherein,DrTo deposit ratio,CpFor depositional model,NcFor necrotic zone,UpFor deposition distribution, bTo reserve the factor.
Wherein,Dr=0.00 indicates 100%,Dr=1.00 indicate 85% to 99%,Dr=2.00 indicate 35% to 85%,Dr=3.00
Indicate < 35%；WithCpIn the case where indicating whether lipiodol deposition is fine and close,Cp=0.50 indicates fine and close,Cp=1 indicates other；?
WithNcIn the case where indicating necrotic zone,Nc=0 indicates 100%,Nc=0.50 indicates 99% to 50%,Nc=1 represent it is other；?
WithUpIn the case where indicating deposition distribution,Up=0.33 indicates tubercle deposition distribution mode,Up=0.50 indicates to diffuse the more of mode
Tubercle lipiodol deposition distribution mode,Up=1.00 indicate other modes.
Disaggregated model is deposited based on the lipiodol indicated by above-mentioned formula, lipiodol deposition ratio, necrotic zone, lipiodol are deposited
Data corresponding to mode and lipiodol deposition distribution carry out operation, the standardization lipiodol deposition point based on medical image constructed
Class method is as shown in Figure 2.
Table 2 is the lipiodol deposition Stratified Strategy of the exponential model proposed.
Fig. 2 is of the present invention heavy based on necrotic zone after lipiodol deposition ratio, lipiodol depositional model, lipiodol and lipiodol
The schematic diagram of the standardization lipiodol deposition classification method based on medical image of product distributed structure, in Fig. 2, the deeper region of color
For the second region group LII, the region LII upper left side is the region Band 1 LI, and the region LII lower right is the area third group LIII
Domain.
The hepatocellular carcinoma that the lipiodol deposition classification method shown according to fig. 2 receives TACE treatment comprising 1302 at one group is suffered from
It is examined in person crowd, every an example patient treats in TACE and carries out imageological examination by the latter moon, by clinician to every
The lipiodol of one patient deposits ratio, and depositional model, deposition distribution and necrotic zone explain, and are proposed according to this method
Exponential type lipiodol deposition disaggregated model carry out operation, according to the lipiodol deposit classification method, the Kaplan- of all patients
Meier survivorship curve is as shown in Figure 3.
By the result in Fig. 3 it can be confirmed that the standardization lipiodol deposits classification method through the invention, tested described
All patients are divided into three groups: Band 1 LI, the second group LII and third group LIII, and each group in card data set
Survival of patients significant difference (significant difference: p < 0.0001, Log-rank are examined).
To sum up, the standardization lipiodol deposition classification method of the invention based on medical image, which can deposit lipiodol, carries out standard
Really classification is gone forward side by side the quantifying of professional etiquette generalized.
Claims (7)
1. a kind of standardization lipiodol based on medical image deposits classification method, which is characterized in that described based on medical image
Standardization lipiodol deposition classification method be according to existing lipiodol deposit ratio classification method, lipiodol necrotic zone classification method,
Lipiodol depositional model classification method and lipiodol deposition distribution classification method these four classification methods are reintegrated, to each
Again segmentation threshold divides subclass for classification method design, formulates corresponding subclass partition mode, to constitute standardization deposition classification
Matrix；
Classification matrix is deposited according to the standardization, using each classification method after segmentation threshold again as independent variable, foundation
Again the subclassification obtained after the segmentation threshold constructs a kind of specification based on medical image as independent variable value range
Change lipiodol deposition index operational model:
。
2. the standardization lipiodol according to claim 1 based on medical image deposits classification method, which is characterized in that describedDrRatio classification method is deposited for lipiodol,CpFor lipiodol depositional model classification method,NcFor lipiodol necrotic zone classification method,Up
For lipiodol deposition distribution classification method, bTo reserve the factor.
3. standardization lipiodol according to claim 2 deposits classification method, which is characterized in that the standardization lipiodol deposition
The segmentation threshold again of exponent arithmetic model is as follows:
Dr=0.00 indicates 100% lipiodol deposition,Dr=1.00 indicate 85% to 99% lipiodol deposition,Dr=2.00 indicate 35% to 86% iodine
Oil deposition,Dr=3.00 indicate < 35% lipiodol deposition.
4. standardization lipiodol according to claim 2 deposits classification method, which is characterized in that the standardization lipiodol deposition
The segmentation threshold again of exponent arithmetic model is as follows:
Cp=0.50 indicates dense form lipiodol deposition,Cp=1 indicates other type lipiodol depositions.
5. standardization lipiodol according to claim 2 deposits classification method, which is characterized in that the standardization lipiodol deposition
The segmentation threshold again of exponent arithmetic model is as follows:
Nc=0 indicates 100% necrosis,Nc=0.50 indicates 50% to 99% necrosis,Nc=1 indicates other necrosis.
6. standardization lipiodol according to claim 2 deposits classification method, which is characterized in that the standardization lipiodol deposition
The segmentation threshold again of exponent arithmetic model is as follows:
Up=0.33 indicates tubercle deposition distribution mode,Up=0.50 indicates to diffuse more tubercle lipiodol deposition distribution modes of mode,Up=1.00 indicate other deposition distribution modes.
7. -6 any standardization lipiodol deposition classification methods based on medical image can be thin to liver according to claim 1
The postoperative lipiodol deposition of born of the same parents cancer patient is accurately divided, and professional etiquette of going forward side by side generalized quantifies.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910336876.6A CN110047576B (en) | 2019-04-25 | 2019-04-25 | Medical image-based normalized iodized oil deposition classification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910336876.6A CN110047576B (en) | 2019-04-25 | 2019-04-25 | Medical image-based normalized iodized oil deposition classification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110047576A true CN110047576A (en) | 2019-07-23 |
| CN110047576B CN110047576B (en) | 2022-11-15 |
Family
ID=67279250
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910336876.6A Active CN110047576B (en) | 2019-04-25 | 2019-04-25 | Medical image-based normalized iodized oil deposition classification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110047576B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040241094A1 (en) * | 2001-09-13 | 2004-12-02 | Hesson Chung | Oily paclitaxel composition and formulation for chemoembolization and preparation method thereof |
| CN108538399A (en) * | 2018-03-22 | 2018-09-14 | 复旦大学 | A kind of magnetic resonance liver cancer cosmetic effect evaluating method and system |
| CN108744243A (en) * | 2018-06-04 | 2018-11-06 | 南京市妇幼保健院 | A kind of controllable pressure hysterosalpingography method |
-
2019
- 2019-04-25 CN CN201910336876.6A patent/CN110047576B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040241094A1 (en) * | 2001-09-13 | 2004-12-02 | Hesson Chung | Oily paclitaxel composition and formulation for chemoembolization and preparation method thereof |
| CN108538399A (en) * | 2018-03-22 | 2018-09-14 | 复旦大学 | A kind of magnetic resonance liver cancer cosmetic effect evaluating method and system |
| CN108744243A (en) * | 2018-06-04 | 2018-11-06 | 南京市妇幼保健院 | A kind of controllable pressure hysterosalpingography method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110047576B (en) | 2022-11-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tsao et al. | Whole brain radiotherapy for the treatment of newly diagnosed multiple brain metastases | |
| Kim et al. | Comprehensive clinical and genetic characterization of hyperprogression based on volumetry in advanced non–small cell lung cancer treated with immune checkpoint inhibitor | |
| Choi et al. | Clinical impact of boost irradiation to pelvic lymph node in uterine cervical cancer treated with definitive chemoradiotherapy | |
| Kaseb et al. | Rhabdomyosarcoma | |
| Sun et al. | Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas | |
| Soyama et al. | A pathological study using 2014 WHO criteria reveals poor prognosis of grade 3 ovarian endometrioid carcinomas | |
| Zhao et al. | Identification of predictors based on drug targets highlights accurate treatment of goserelin in breast and prostate cancer | |
| WO2023006843A1 (en) | Prediction of brcaness/homologous recombination deficiency of breast tumors on digitalized slides | |
| Chen et al. | Chemotherapy plus radiotherapy versus chemotherapy alone for patients with peripheral T-cell lymphoma, not otherwise specified | |
| Lin et al. | Standard pancreatoduodenectomy versus extended pancreatoduodenectomy with modified retroperitoneal nerve resection in patients with pancreatic head cancer: a multicenter randomized controlled trial | |
| Sinn et al. | Patterns and outcomes in hepatocellular carcinoma patients with portal vein invasion: a multicenter prospective cohort study | |
| Scharl et al. | Is there a benefit for adjuvant radio (chemo) therapy in early cervical cancer? Results from a population-based study | |
| Opdam et al. | Limiting systemic endocrine overtreatment in postmenopausal breast cancer patients with an ultralow classification of the 70-gene signature | |
| CN110047576A (en) | A kind of standardization lipiodol deposition classification method based on medical image | |
| Lau et al. | Concomitant low‐dose cisplatin and three‐dimensional conformal radiotherapy for locally advanced squamous cell carcinoma of the head and neck: analysis of survival and toxicity | |
| Torricelli et al. | The relevance of family history taking in the detection and management of Birt-Hogg-Dube syndrome | |
| Back et al. | Understanding the revised fourth edition of the World Health Organization classification of tumours of the central nervous system (2016) for clinical decision-making: a guide for oncologists managing patients with glioma | |
| Ferguson et al. | Triple-negative breast cancer: what is known about it | |
| Liu et al. | Chronic myeloid leukemia blast crisis presented with AML of t (9; 22) and t (3; 14) mimicking acute lymphocytic leukemia | |
| Meinhardt et al. | Solitary Primary Intracranial Extramedullary Plasmacytoma With Lymph Node Metastasis | |
| Tran et al. | Renal remodeling after abdominal radiation therapy: Parenchymal and functional changes | |
| Hong et al. | Impact of hyperglycemia on survival and infection-related adverse events in patients with metastatic colorectal cancer who were receiving palliative chemotherapy | |
| Pirozzi et al. | Incidental finding of non-Hodgkin's lymphoma in a patient affected by castration-sensitive prostate cancer: A case report | |
| Guglieri-López et al. | Effectiveness, toxicity, and economic evaluation of vinflunine for the treatment of patients with transitional cell carcinoma in the Spanish outpatient setting | |
| Dumas et al. | The French Early Breast Cancer Cohort (FRESH): a resource for breast cancer research and evaluations of oncology practices based on the French National Healthcare System Database (SNDS). Cancers (Basel) 2022; 14 (11): 2671 |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |