CN113496768B - Pathological tissue-based dual comprehensive tumor analysis system and application - Google Patents
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Abstract
The invention relates to a pathological tissue-based dual comprehensive tumor analysis system and application. The present invention provides a computer system comprising a processor and a storage device storing computer executable code, wherein when executed at the processor, the computer executable code is configured to: providing a prognosis evaluation result and/or a treatment regimen selection for a patient based on the following data from the patient's tumor tissue sample: i) Chromatin structure typing data and DNA ploidy data, or ii) chromatin structure typing data and tumor stroma ratio data. The present invention provides for more accurate prognosis of tumor patients and more appropriate treatment timing and treatment regimen selection.
Description
Technical Field
The invention relates to the field of artificial intelligence detection and analysis based on pathological tissues. In particular, the invention relates to pathological tissue-based tumor analysis systems and applications that may be used for prognosis evaluation (e.g., surgical prognosis evaluation and/or recurrence risk evaluation, etc.) and/or selection of treatment regimens (e.g., determining whether a particular treatment regimen is appropriate for use, including surgery and/or chemotherapy, etc.) for a tumor patient.
Background
Prognosis of a disease involves the prediction of the progression of the disease. In general, the efficacy, prognosis, recovery, recurrence or extent of progression of a disease can be assessed in view of the patient's clinical manifestation, test results, imaging, etiology, pathology, and rules of illness. Disease prognosis is related to a variety of factors such as the timing of treatment of the patient, the extent of occurrence of the disease, the medical level, the combined disease, constitution, age, cognitive ability to the disease, whether to continue treatment, and the like. The major factors affecting prognosis may vary from disease to disease and from patient to patient, and thus there is a need to study risk factors affecting diseases such as tumors and to determine appropriate treatment regimens for specific diseases and for specific choices.
Taking colorectal cancer as an example, colorectal cancer is reported to have a prevalence and mortality in the 2016 united states at positions 4 and 2 of the cancer spectrum, respectively; in 2015, colorectal cancer incidence and mortality rate in China are all 5 th, but the ratio of death to new incidence (50.8%) is obviously higher than that in the United states (36.6%) (Yao Hongwei, wu Hongwei, liu Yinhua, journal of China surgery, 2017,55 (1): 24-27).
The united states joint commission on cancer (AJCC) eighth edition TNM staging system divides colorectal cancer into stages 0, I, II (including IIA, IIB, IIC), III (including IIIA, IIIB, IIIC), IV (including IVA, IVB, IVC) (see the AJCC eighth edition colorectal cancer staging system definition and description for details).
It is reported that the post-operative adjuvant chemotherapy benefit rate of patients with stage II colorectal cancer is not more than 5%, and domestic and foreign guidelines suggest that stage II patients decide on adjuvant therapy decisions based on postoperative recurrence risk. The high risk factors of the patient in the II stage at present comprise T4 (IIB and IIC stages), poor histological differentiation (grade 3/4, MSI-H is not included), vascular infiltration, nerve infiltration, intestinal obstruction, perforation of tumor parts, positive or unknown incisional margin, insufficient incisional margin safety distance and less than 12 lymph nodes to be examined. The low risk factors are MSI-H (microsatellite highly unstable) or dMMR (mismatch repair protein deletion). Previous reports also indicate that combining pathological grading with tumor differentiation does not predict well the prognosis of stage II colorectal cancer and instruct whether or not to use combination chemotherapy.
Chinese clinical society of oncology (CSCO) colorectal cancer diagnosis and treatment guide 2019.v1
3.1.1.3 postoperative adjuvant chemotherapy
At present, the clinical practice generally considers that the existing risk assessment factors are not provided with reliable evidence-based data, the judging method is relatively subjective, or the crowd who benefit is very little (the detection rate of MSI-H in colorectal cancer in stage II is only 10% -15%). Therefore, methods suitable for more patients with stage II colorectal cancer and capable of more objectively assessing prognosis risks and the benefits of chemotherapy are still being explored clinically.
There are studies showing that 12gene-RS is risk scored (recurrence score) by assessing the expression of 7 genes and 5 housekeeping genes associated with colorectal cancer recurrence risk, the risk ratio between the high risk group and the low risk group being 1.47 (95% CI,1.01-2.14; P < 0.046). J Clin Oncol 29:4611-4619.
It is reported in the literature that ColoProg, by combining DNA ploidy analysis with tumor stroma ratio analysis, can be used to evaluate prognosis of patients with stage II colorectal cancer, and that the use of ColoProg can be used to classify patients into three recurrence risks, high, medium and low, with a risk ratio hr=2.9 (95% ci 1.73-5.03) between the high and low risk groups, P <0.001.Annals of Oncology 0:1-8,2018. However, the significance of the guidance for clinical treatment remains to be improved, the total survival rate (OS) of the low-risk group for 5 years is 79%, the OS of the medium-risk group for 5 years is 72%, and the OS of the high-risk group for 5 years is 63%; the Cancer Specific Survival (CSS) of the high, medium and low risk three groups are respectively: 90%,83% and 73%. The difference between the packets remains to be increased.
Thus, there is a need for intensive research into tumors such as colorectal cancer and the like to provide more accurate prognosis evaluation for individualization and to select the optimal treatment regimen.
Disclosure of Invention
The inventors have conducted intensive studies on various risk factors for tumors such as colorectal cancer, and have found that more accurate prognosis and more accurate treatment regimen selection can be provided by a combination of a few key factors, thereby completing the present invention. In some embodiments, the invention is particularly suited for directly providing prognostic decisions and/or selection of treatment regimens using a computer system, including, for example, determining whether a patient is operating and post-operatively suitable for combination chemotherapy, and the like.
In some embodiments, the invention relates to one or more of the following:
1.a computer system comprising a processor and a storage device storing computer executable code, wherein the computer executable code, when executed at the processor, is configured to:
providing a prognosis evaluation result and/or a treatment regimen selection for a patient based on the following data from the patient's tumor tissue sample: i) Chromatin structure typing data and DNA ploidy data, or ii) chromatin structure typing data and tumor stroma ratio data,
wherein for solid tumors such as colorectal cancer, diploid score 0, non-diploid score 1, chromatin homoplasmy score 0, chromatin heteroplasmy score 1, low interstitium ratio score 0, high interstitium ratio score 1, wherein:
1) A total score of 0 is judged to be low risk, a total score of 1 is judged to be medium risk, and a total score of 2 is judged to be high risk; and/or
2) Wherein patients with a total score of 0 were judged to be suitable for single postoperative observation, and patients with a total score of 2 were judged to be suitable for combination chemotherapy.
2. The computer system of item 1, wherein:
1) DNA ploidy data from a patient tumor tissue sample includes determining the DNA ploidy in the tumor tissue as diploid or non-diploid, which may be determined by observation, e.g., by microscopy, single cell absorbance or fluorescence counting based on pathological sections, flow cytometry, or by computer image analysis, preferably, e.g., by DNA ploidy based on automatic image recognition, and/or
2) Chromatin structure typing data from a patient tumor tissue sample includes determining a pattern of chromatin in the tumor tissue as chromatin homogeneous or chromatin heterogeneous, which may be determined by, for example, microscopic observation or by computer image analysis based on pathological sections, preferably by, for example, chromatin structure typing based on automatic image recognition.
3. The computer system of item 1 or 2, wherein the tumor stroma ratio data from the patient tumor tissue sample comprises a determination of the proportion of the stroma component in the tumor tissue as a low stroma ratio or a high stroma ratio, which may be determined by, for example, microscopic observation or by computer image analysis based on pathological sections, preferably by, for example, a determination of the stroma ratio based on automatic image recognition.
4. The computer system of any of clauses 1-3, wherein the tumor is a solid tumor, including, for example, colorectal cancer.
5. The computer system of item 4, wherein the tumor is colorectal cancer, including, for example, post-operative colorectal cancer, such as any stage of TNM staging colorectal cancer, including, for example, stage 0, stage I, stage II (including stage IIA, IIB, IIC), preferably stage II colorectal cancer.
6. The computer system of any one of items 1-5, wherein:
1) Determining DNA ploidy in tumor tissue as diploid or non-diploid by computer image analysis, e.g., DNA ploidy, e.g., determining a cumulative optical density value of nuclei calculated from DNA ploidy analysis software as diploid or non-diploid; and/or
2) The chromatin pattern in the tumour tissue is determined to be chromatin homogeneous or chromatin heterogeneous by computer image analysis, e.g. chromatin structure typing analysis software, e.g. chromatin homogeneous (CHO, > 0.044) or chromatin heterogeneous (CHE, < 0.044) according to an appropriate threshold, e.g. a threshold calculated by chromatin structure typing analysis software as 0.044.
7. The computer system of any of clauses 1-6, wherein the proportion of the interstitial component in the tumor tissue is determined by computer image analysis, e.g., by the interstice flow analysis software, as low or high interstitial ratio, e.g., as low interstitial ratio set (+.50%) and high interstitial ratio set (> 50%) according to a suitable threshold, e.g., a 50% threshold.
8. A computer readable medium having stored thereon instructions which, when executed by a processor, cause the processor to execute the computer executable code defined in any one of items 1-7.
9. Use of a reagent for determining the following data from a patient tumor tissue sample in the preparation of a kit providing a prognosis evaluation result and/or a selection of treatment regimens for said patient: i) Chromatin structure typing data and DNA ploidy data, or ii) chromatin structure typing data and tumor stroma ratio data, wherein the tumor is a solid tumor, including, for example, colorectal cancer.
In some embodiments, the present invention may be implemented by, for example, hardware apparatus 500 illustrated in fig. 6. Hardware device 500 includes a processor 506. The processor 506 may be a single processing unit or a plurality of processing units for performing the processes described herein. The apparatus 500 may further comprise an input unit 502 receiving the signal, and an output unit 504 providing the signal. The input unit 502 and the output unit 504 may be arranged as a single or separate units. The apparatus 500 may include at least one readable storage medium 508, such as EEPROM, flash memory, and/or a hard disk drive. The readable storage medium 508 includes a computer program 510, the computer program 510 comprising code/computer readable instructions which, when executed by the processor 506 in the apparatus 500, enable the hardware apparatus 500 to perform, for example, the processes described herein and variations thereof. The computer program 510 may be configured as computer program code having, for example, a computer program module 510A, a module 510B, a module 510C architecture for performing the steps described herein. The processor may be a single CPU or may include two or more processing units. The computer program may be carried by a computer program product connected to the processor. The computer program product may include a computer readable medium having a computer program stored thereon. For example, the computer program product may be flash memory, random Access Memory (RAM), read Only Memory (ROM), EEPROM. The computer program modules described above may be distributed in different computer program products in the form of memory within the UE. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, when executed by the processor, create means for implementing the functions/acts. Additionally, aspects described herein may take the form of a computer program product on a computer-readable medium having instructions stored thereon for use by or in connection with an instruction execution system.
In some embodiments, the invention provides methods and related products, such as kits (kit), for prognosis evaluation results and/or selection of treatment regimens for patients, which may include, for example, reagents for determining data from tumor tissue samples of patients. Reagents for determining data from a patient tumor tissue sample are used in a broad sense herein and may include, for example, reagents for pre-treating a patient tumor tissue sample. In some embodiments, the data from a patient tumor tissue sample may include, for example, i) chromatin structure typing data and DNA ploidy data or ii) chromatin structure typing data and tumor stroma ratio data. In some embodiments, the invention provides compositions or kits that can be used to provide prognostic assessment results for a patient and/or selection of treatment regimens, which can include, for example, reagents for pre-treating a tumor tissue sample from a patient to pre-treat the sample to determine i) chromatin structure typing data and DNA ploidy data or ii) chromatin structure typing data and tumor interstitial ratio data. In some embodiments, the invention provides the use of a reagent for pre-treating a tumor tissue sample from a patient, wherein the reagent is used to pre-treat a tumor tissue sample from a patient to determine i) chromatin structure typing data and DNA ploidy data or ii) chromatin structure typing data and tumor interstitial ratio data of the sample, in the preparation of a product, such as a kit, that provides a prognosis evaluation result and/or a selection of a treatment regimen for the patient. In some embodiments, the tumor referred to herein can be a solid tumor, including, for example, colorectal cancer. In some embodiments, the chromatin structure typing, DNA ploidy and interstitial ratio analysis methods are not particularly limited and may be performed using any method known in the art. In some embodiments, the kits of the invention may include reagents, software and/or devices suitable for determining chromatin structure typing data, DNA ploidy data and/or tumor stroma ratio data from a tumor tissue sample of a patient, such as reagents and devices suitable for performing tissue sections, such as reagents and devices suitable for performing staining (e.g., HE staining), such as reagents, software and/or devices suitable for detecting chromatin structure typing by epigenetic methods, such as those available from majora technologies (guangzhou) limited, chromatin structure typing for technical support by software developers, lulu group limited, uk, DNA ploidy and stroma ratio analysis software, such as reagents, software and/or devices suitable for performing ploidy analysis of flow cytometry and other liquid-based tumor cells. In some embodiments, the reagents of the invention may include, for example, staining solutions, buffers, washing solutions, and the like. In some embodiments, the reagents in the kits of the invention may be placed in separate containers, or the same reagent in the same container.
In some embodiments, the risk assessment of the present invention combines i) a cellular chromatin structure typing analysis and a DNA ploidy analysis, or II) a cellular chromatin structure typing analysis, a chromatin structure typing and a interstitial ratio analysis, to provide a better prediction of the risk of relapse in patients with stage II colorectal cancer.
Cell chromatin structure typing analysis: patients can be divided into two groups, chromatin homogeneous (CHO, > 0.044) and chromatin heterogeneous (CHE, < 0.044) according to a threshold of 0.044. Chromatin homogeneous group patients have better prognosis, low risk; chromatin heterogeneous patients have a poor prognosis and high risk.
DNA ploidy analysis: patients can be divided into two groups, diploid and non-diploid. The prognosis of diploid patients is better, low risk; non-diploid patients have a poor prognosis and high risk.
Interstitial ratio analysis: patients can be divided into low-to-interstitium (.ltoreq.50%) and high-to-interstitium (> 50%) according to a 50% threshold. Low interstitium is better than patient prognosis, low risk; high interstitium is worse than patient prognosis, high risk.
An exemplary combination algorithm of i) analysis of cell chromatin structure typing and analysis of DNA ploidy, combined with technical methods for prognosis evaluation of stage II colorectal cancer and indices for prediction prognosis is as follows:
cell chromatin structure typing analysis: low risk score 0, high risk score 1
DNA ploidy analysis: low risk score 0, high risk score 1
Low risk: total score=0 score
Risk of (1): total score = 1 score
High risk: the total score was 2.
Or II) a combination of cytochromatin structure typing analysis and interstitial ratio analysis for the prognosis evaluation of stage II colorectal cancer, and an exemplary combination algorithm of indicators of prognosis as follows:
cell chromatin structure typing analysis: low risk score 0, high risk score 1
DNA ploidy analysis: low risk score 0, high risk score 1
Low risk: total score=0 score
Risk of (1): total score = 1 score
High risk: the total score was 2.
The prognosis evaluation effect of solid tumors such as colorectal cancer in stage II after the index combination is obviously superior to that of the prior art or the combination. The evaluation effect of the index combination of the present invention is compared with other indexes, see tables a, B and C.
Table a: in completed clinical studies, the prognostic predictive value of the index combinations of the present invention is compared to other prognostic indices or other combinations
TABLE B parameter comparison of the prognostic evaluation value of the index combinations of the invention with conventional prognostic markers
Table C: the prognostic evaluation value of the index combination of the invention is compared with parameters calculated by combining a plurality of indexes in other researches
Wherein:
12gene-RS (detection BGN, FAP, INHBA, MKI, MYC, MYBL2, GADD45B, atp5e.gpx1, PGK1, VADC2, UBB expression levels of 12gene mRNA and colorectal cancer recurrence risk scores calculated by corresponding algorithms): high risk group versus low risk group hr=1.47 (95% ci 1.01-2.14; p=0.046) (ref: J Clin Oncol 29:4611-4619, 2011);
colopprog: high risk group vs low risk group hr=2.9 (95% ci 95% 1.73-5.03), P <0.001. (ref: annals of Oncology 0:1-8,2018);
Four-miRNA classifer (the recurrence risk of colorectal cancer patients is estimated by detecting the expression levels of Four miRNAs of hsa-miR-5010-3p, hsa-miR-5100, hsa-miR-656-3p and hsa-miR-671-3p and a corresponding algorithm): high risk group versus low risk group hr=3.16 (95% ci1.36-7.33, p=0.007) (ref: scientific Report 8:6157, 2018);
CMS (consensus molecular subtypes, CMS1, CMS2, CMS3, CMS 4) patients with intestinal cancer are classified into four types by MSI, CIMP, BRAF mutation, SCNA, KRAS, interstitial infiltration, TGF activity, etc.: CMS4 ratio cms1hr=1.77 (95% ci 1.34-2.34, p < 0.001) (ref: nat med.21 (11): 1350-1356, 2015).
The detection method is completed by adopting optical density automatic analysis software, and the result is obtained by adopting an automatic pathological image analysis technology through the intermittent imaging.
Those skilled in the art will readily appreciate that DNA ploidy data, chromatin structure typing data and tumor stroma ratio data may be obtained by various methods known in the art, including, for example, microscopic examination of pathological sections, and the like. For example, for DNA ploidy, ploidy analysis of other liquid-based tumor cells can be performed by flow cytometry; for chromatin structure typing, detection may be by other epigenetic methods.
Drawings
Fig. 1: chromatin structure typing combined DNA ploidy survival analysis: in stage II tumor patients, kaplan-Meier curves of disease-free survival (DFS) of 3 groups of patients (chromatin homoplasmic + diploid, chromatin homoplasmic + non-diploid or chromatin heteroplasmic + diploid, chromatin heteroplasmic + non-diploid) calculated by chromatin structure typing in combination with DNA ploidy.
Fig. 2: chromatin structure typing combined interstitial specific survival analysis: in stage II tumor patients, kaplan-Meier curves of disease-free survival (DFS) of 3 groups of patients (chromatin homoplasmy+hypo-interstitium ratio, chromatin homoplasmy+hyperinterstitium ratio or chromatin hetero+hypo-interstitium ratio, chromatin hetero+hyperinterstitium ratio) obtained by chromatin structure typing combined interstitium ratio were used.
Fig. 3: DNA ploidy survival analysis: DNA ploidy survival assay results.
Fig. 4: chromatin structure typing survival analysis: chromatin structure typing survival analysis results.
Fig. 5: interstitial specific survival analysis: results of the interstitial ratio survival analysis.
Fig. 6: a block diagram of an example hardware arrangement 500 that may be used to implement the present invention.
Detailed Description
The study was a retrospective analysis performed at the Beijing university tumor Hospital, analyzing follow-up data and chromatin structure typing DNA ploidy interstitial ratio detection data of 188 continuously collected stage II colorectal cancer patients. And collecting indexes (including age, sex, tumor position, T stage, pathological typing, pathological grading, auxiliary treatment information and MSI (microsatellite instability)) related to prognosis of the patient, and relapse information and survival information of the patient. The risk assessment is performed by adopting chromatin structure typing and DNA ploidy, and three kinds of analysis software (all available from Mei 'ao technology (Guangzhou) limited company, provided by software developer Yi' un group limited company, UK) respectively, and then the K-M survival curve analysis is performed after 3 kinds of analysis software are combined.
Tumor sampling
The pathologist selects the most representative tumor paraffin tissue, and cuts out 2 tissue slices with the thickness of 50 μm and 4 tissue slices with the thickness of 5 μm for detection of chromatin structure typing, DNA ploidy and interstitial ratio.
Preparation of cell Nuclear coating
HE staining was performed using 15 μm sections for determining the circled tumor area. The tumor region in the 50 μm section was removed according to the circled tumor region. After gradient ethanol dewaxing treatment, the cell suspension was filtered using a 60- μm nylon mesh filter after digestion with 0.5mg/ml protease VIII. After discarding the supernatant, the filtered cells were resuspended in PBS, 100. Mu.l of the suspension was pipetted for centrifugation, the smears were air-dried and then fixed with formaldehyde, followed by Fulger staining. The stained nuclear coating was scanned using a digital pathology scanner.
Chromatin structure typing assay
The analysis of the nuclear coating scan image was performed using chromatin structure typing analysis software (available from Mei-ao technology (Guangzhou) and technical support was provided by the software developer, kiku group Limited), and after classifying the scanned nuclei, the chromatin values of each patient were obtained by analyzing and calculating four dimensions (GLEM-4D) of the size of the epithelial nuclei, gray values of each pixel point, and entropy values calculated in different sampling windows, and were classified into two groups of chromatin homogeneity (CHO, > 0.044) and chromatin heterogeneity (CHE, < 0.044) according to a threshold of 0.044.
DNA ploidy detection assay
As in the first step of the analysis of chromatin structure, analysis software (available from Meinao technology (Guangzhou) limited company, which provides technical support by software developers, eu, inc. of Europe, UK) is adopted to classify the cell nuclei, and then the cumulative optical density value of the epithelial cell nuclei is calculated, and the software can be automatically classified into diploids, tetraploids, aneuploids and polyploids according to the optical density value, wherein in the study, the tetraploids, aneuploids and polyploids are classified into non-diploids.
Interstitial ratio detection analysis
Sections of 5 μm were routinely stained for H & E and scanned using an Aperio AT2 scanner. The scanned image was opened in interstitial ratio analysis software (available from the company of majora technologies, guangzhou, which provides technical support by the software developer, lulu, kingdom) and the proportion of interstitial in the tumor area was automatically calculated by the pathologist after the area was circled in the software. The low-interstitial ratio group (.ltoreq.50%) and the high-interstitial ratio group (> 50%) were classified according to a 50% threshold.
The chromatin structure typing + DNA ploidy combining algorithm is as follows:
chromatin structure typing: low risk score 0, high risk score 1
DNA ploidy: low risk score 0, high risk score 1
Low risk: two indices add total score=0 score
Risk of (1): total score of two indices added = 1 score
High risk: the two indices add up to a total score = 2.
The chromatin structure typing + matrix ratio combination algorithm is as follows:
chromatin structure typing: low risk score 0, high risk score 1
Interstitial ratio: low risk score 0, high risk score 1
Low risk: two indices add total score=0 score
Risk of (1): total score of two indices added = 1 score
High risk: the two indices add up to a total score = 2.
Experimental results
Table 1 clinical characteristics
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MSI-L: microsatellite low instability; MSI-H: microsatellites are highly unstable; MSS: microsatellite stabilization
TABLE 2 one-factor analysis of total survival (OS) and disease-free survival (DFS) prognostic factors for II patients
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MSI-L: microsatellite low instability; MSI-H: microsatellites are highly unstable; MSS: the microsatellite is stable;
OS, total Survival (Overall survivinal); DFS, disease-Free Survival (Disease-Free Survival)
Conclusion: as a result of the one-factor analysis, it was found that i) a cell chromatin structure typing analysis (chromatin structure typing) and a DNA ploidy analysis (DNA ploidy), or II) a cell chromatin structure typing analysis (chromatin structure typing) and a interstitial ratio analysis (interstitial ratio) were combined, and DFS stratification was best for patients with stage II colorectal cancer, and the risk ratios of the high-risk group to the low-risk group were hr= 4.632 (95% ci 1.995-10.752, p < 0.001) and hr=4.161 (95% ci 1.635-10.595, p=0.003), respectively.
Compared with other methods, such as ColoProg, etc., the chromatin structure typing combined DNA ploidy analysis can divide patients into three groups of high, medium and low risk, and the total survival (OS) of the three groups is 90.8%,87.9% and 68.6% respectively; three groups of disease progression free survival (DFS) were 89.7%,81.8%,60.0%, respectively; the proportion of the high, medium and low-risk groups is 46.3%,35.1% and 18.6% respectively. Compared with other methods such as ColoProg, the method has better layering effect of chromatin structure typing combined with DNA ploidy analysis, and more patients (about 13%) can be classified into a low-risk group, so that the patients can avoid toxic and side effects caused by single-drug chemotherapy or even combined chemotherapy.
Compared with other methods, such as ColoProg, etc., the chromatin structure typing combined interstitial ratio analysis can divide patients into three groups of high, medium and low risk, and the total survival (OS) of the three groups is 90.0%,77.8% and 69.2% respectively; three groups of disease progression free survival (DFS) were 86.9%,73.3%,53.8%, respectively; the proportion of the high, medium and low-risk groups is 69.1%,23.9% and 6.9% respectively. Compared with other methods such as ColoProg, the chromatin structure typing combined matrix has better analysis and stratification effects, and more patients (about 35%) can be classified into a low-risk group, so that the patients can avoid toxic and side effects caused by single-drug chemotherapy or even combined chemotherapy.
Claims (11)
1.A computer system comprising a processor and a storage device storing computer executable code, wherein the computer executable code, when executed at the processor, is configured to:
providing a prognosis evaluation result and/or a treatment regimen selection for a patient based on the following data from the patient's tumor tissue sample: i) The data consists of chromatin structure typing data and DNA ploidy data, or ii) the data consists of chromatin structure typing data and tumor stroma ratio data,
wherein the tumor is colorectal cancer, diploid score 0 in DNA ploidy data, non-diploid score 1, chromatin score 0 in chromatin structure typing data, chromatin heterosis score 1, low interstitial ratio score 0 in tumor interstitial ratio data, high interstitial ratio score 1, wherein:
1) A total score of 0 is judged to be low risk, a total score of 1 is judged to be medium risk, and a total score of 2 is judged to be high risk; and/or
2) Wherein patients with a total score of 0 were judged to be suitable for single postoperative observation, and patients with a total score of 2 were judged to be suitable for combination chemotherapy.
2. The computer system of claim 1, wherein:
1) DNA ploidy data from a patient's tumor tissue sample includes determining the DNA ploidy in the tumor tissue as diploid or non-diploid by either microscopic observation based on pathological sections, single cell absorbance or fluorescence counting, flow cytometry, or by computer image analysis,
2) Chromatin structure typing data from a patient tumor tissue sample includes determining a chromatin pattern in the tumor tissue as chromatin homogeneous or chromatin heterogeneous, as determined by microscopic observation or by computer image analysis based on pathological sections, and/or
3) Tumor interstitial ratio data from a patient tumor tissue sample includes determining the proportion of interstitial components in the tumor tissue as either low or high interstitial ratios, as determined by microscopic observation or by computer image analysis based on pathological sections.
3. The computer system of claim 1, wherein:
DNA ploidy determination, chromatin structure typing determination and/or tumor interstitial ratio determination are performed based on automatic image recognition.
4. The computer system of claim 1, wherein the tumor is post-operative colorectal cancer.
5. The computer system of claim 1, wherein the tumor is any stage of colorectal cancer in TNM staging.
6. The computer system of claim 1, wherein the tumor is stage 0, stage I, or stage II colorectal cancer.
7. The computer system of claim 1, wherein the tumor is stage IIA, IIB, or IIC colorectal cancer.
8. The computer system of any of claims 1-3, wherein:
1) Determining DNA ploidy in tumor tissue as diploid or non-diploid by computer image analysis;
2) Determining a chromatin pattern in the tumor tissue as chromatin homogeneous or chromatin heterogeneous by computer image analysis; and/or
3) And judging the proportion of the interstitial components in the tumor tissue as a low interstitial ratio or a high interstitial ratio through computer image analysis.
9. The computer system of any of claims 1-3, wherein
1) Determining the DNA ploidy as diploid or non-diploid based on the nuclear cumulative optical density value calculated by the DNA ploidy analysis software, 2) determining greater than or equal to 0.044 as chromatin homogeneity based on a threshold value of 0.044 calculated by the chromatin structure typing analysis software, determining less than 0.044 as chromatin heterogeneity, and/or
3) According to a 50% threshold of the interstitial ratio analysis software, less than or equal to 50% is judged as a low interstitial ratio, and > 50% is judged as a high interstitial ratio.
10. A computer readable medium having instructions stored thereon which, when executed by a processor, cause the processor to execute the computer executable code defined in any one of claims 1 to 9.
11. Use of a reagent for determining the following data from a patient tumor tissue sample in the preparation of a kit providing a prognosis evaluation result and/or a selection of treatment regimens for said patient: i) The data consists of chromatin structure typing data and DNA ploidy data, or ii) the data consists of chromatin structure typing data and tumor stroma ratio data, wherein the tumor is colorectal cancer, the prognosis evaluation result and/or treatment regimen is selected as follows: diploid score 0 in DNA ploidy data, non-diploid score 1, chromatin score 0 in chromatin structure typing data, chromatin heterosis score 1, low interstitial ratio score 0 in tumor interstitial ratio data, high interstitial ratio score 1, wherein:
1) A total score of 0 is judged to be low risk, a total score of 1 is judged to be medium risk, and a total score of 2 is judged to be high risk; and/or
2) Wherein patients with a total score of 0 were judged to be suitable for single postoperative observation, and patients with a total score of 2 were judged to be suitable for combination chemotherapy.
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| CN105144179A (en) * | 2013-01-29 | 2015-12-09 | 分子健康股份有限公司 | Systems and methods for clinical decision support |
| CN109448858A (en) * | 2018-10-30 | 2019-03-08 | 中山大学肿瘤防治中心 | Colorectal cancer medical data processing method and processing device, storage medium, electronic equipment |
| CN109493926A (en) * | 2018-10-30 | 2019-03-19 | 中山大学肿瘤防治中心 | Processing method, device, medium and the electronic equipment of colorectal cancer medical data |
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| CN105144179A (en) * | 2013-01-29 | 2015-12-09 | 分子健康股份有限公司 | Systems and methods for clinical decision support |
| CN109448858A (en) * | 2018-10-30 | 2019-03-08 | 中山大学肿瘤防治中心 | Colorectal cancer medical data processing method and processing device, storage medium, electronic equipment |
| CN109493926A (en) * | 2018-10-30 | 2019-03-19 | 中山大学肿瘤防治中心 | Processing method, device, medium and the electronic equipment of colorectal cancer medical data |
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