RU2523138C1 - Method for prediction of risk of developing progression of disease following radiofrequency thermoablation of hepatic metastases from colorectal cancer - Google Patents

Abstract

FIELD: medicine.

SUBSTANCE: computed tomography or magnetic resonant tomography of the liver precedes radiofrequency thermoablation of metastases to determine: the quantity and dimensions of the metastases of colorectal cancer, as well as to measure a blood carcinoembryonal antigen and a cancer antigen 19-9, and to calculate a probability of the progression of the disease by formula. The derived value provides a basis to detect a low or high risk of developing the progression of the disease.

EFFECT: method provides predicting a risk of developing the progression of the disease effectively before radiofrequency thermoablation by determining the pre-operative computed tomography findings.

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Description

The invention relates to medicine, namely to oncology. In recent years, there has been an increase in the incidence of colorectal cancer, the liver is the main target organ in the path of hematogenous metastasis. An incorrect interpretation of the results of instrumental research methods after performing radiofrequency ablation of liver metastases often leads to diagnostic errors (1, 2). A feature of the method of radiofrequency ablation is that the tumor tissue subjected to thermal destruction is not removed from the body, but remains in it and is subsequently gradually replaced by fibrous tissue (3). Assessing the effectiveness of radiofrequency ablation in the first months after surgery is one of the key tasks of applying the technology (2).

A known method for predicting the risk of progression of the disease after surgical treatment of patients with liver metastases according to computed tomography. The principle of this technique is that after performing these examinations, it is estimated: the number, size and localization of metastatic formations in the liver, which affects the prognosis of the disease (4, 5).

The disadvantage of this method is that only the volume of tumor tissue and the localization of metastases are evaluated, but the possible immunological response of the patient’s body is not taken into account, accordingly, the growth rate of existing metastases cannot be predicted, and therefore it is not possible to objectively predict the risk of progression after radiofrequency thermal ablation.

The RECIST criterion is often used to predict the risk of disease progression and evaluate the effect of the treatment: after performing an ultrasound examination of the liver or computed tomography, tumor lesions are detected, for ultrasound examination 20 mm in diameter or more, for computed tomography 10 mm in diameter or more, it is not used more than 5 tumor foci in the organ or 10 tumor foci in a particular patient, the diameter of these metastases is measured and the obtained data are statistically calculated iyam thus calculated risk of disease progression after RFA (6,7).

The disadvantage of this technique is the lack of consideration of possible data of immunological blood markers, an increase in the level of cancer-embryonic antigen and cancer antigen 19-9, and the disadvantages of this technique include the fact that some metastatic foci cannot be measured or their measurement is difficult. This method is taken as a prototype.

The purpose of this method is to develop an objective method for predicting the risk of progression of the disease after performing radiofrequency thermal ablation of colorectal cancer metastases in the liver, which allows the choice of tactics for subsequent treatment.

This goal is achieved by the fact that prior to surgery, the level of cancer-embryonic antigen and cancer antigen 19-9 in the blood is additionally determined and the probability of disease progression P is calculated by the formula:

P = ^1/1 + 2.71 ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

where P is the probability that a disease progression will occur after radiofrequency thermal ablation of colorectal cancer metastases to the liver in fractions of units; X1 - the number of metastases of colorectal cancer; X2 - preoperative value of cancer-embryonic antigen; X3 - preoperative value of cancer antigen 19-9; X4 - the number of colorectal cancer metastases in size from 2 to 3 cm; and when a P value less than or equal to 0.69 is obtained, the risk of developing a process progression is low, and when a P value is greater than 0.69, the risk of developing a process progression is high.

The method is implemented as follows: before performing radiofrequency ablation, the patient undergoes computed tomography of the liver, in which they determine: the number of metastases of colorectal cancer and their sizes in cm. Then, the level of cancer-embryonic antigen and the level of cancer antigen 19-9 are determined in venous blood. Substitute the obtained data in the formula:

P = ^1/1 + 2.71 ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

where P is the probability that a disease progression will occur after radiofrequency thermal ablation of colorectal cancer metastases to the liver in fractions of units; X1 - the number of metastases of colorectal cancer; X2 - preoperative value of cancer-embryonic antigen; X3 - preoperative value of cancer antigen 19-9; X4 - the number of colorectal cancer metastases in size from 2 to 3 cm; and when a P value less than or equal to 0.69 is obtained, the risk of developing a process progression is low, and when a P value is greater than 0.69, the risk of developing a process progression is high.

Since it is technically impossible to model a dichotomous dependent variable, taking into account an infinite number of values of the predictors X, the regression problem is formulated as follows: instead of a dichotomous variable, a continuous variable is predicted with values in the interval from 0 to 1 for any predictor values using the logit transformation:

P = ^1/1 + 2.71 ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

where P is the probability that the disease will progress after radiofrequency ablation of colorectal cancer metastases in the liver, the value 2.71 is the basis of the natural logarithms and ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4 , 68 * X4-12,03)} multiple linear regression formula, where X1 is the number of metastases of colorectal cancer, X2 is the preoperative value of cancer-embryonic antigen, X3 is the preoperative value of cancer antigen 19-9, X4 is the number of metastases of colorectal cancer from 2 up to 3 cm.

The model included 93 patients of group 1a with bilobar metastases of colorectal cancer identified after removal of the primary tumor. The creation of a mathematical model was aimed at determining the risk factors for relapse in the field of radiofrequency ablation and the appearance of new metastases according to computed tomography of the abdominal cavity, with contrast enhancement after thermal ablation.

In this study, the mathematical model was built in a logistic regression module using the Wald algorithm with step-by-step exception in the computer program Statistical Package for Social Science. Using this method, 11 predictors were initially calculated. The relative contribution of various predictors was expressed by the value of the following criteria:

1. Statistics Wald.

2. Statistical criterion X square.

3. The value of the standardized regression coefficient.

As a result, a model was obtained that included 4 of the most significant predictors (X1-X4):

1. The number of detected metastases.

2. The value of cancer-embryonic antigen before surgery in ng / ml.

3. The value of cancer antigen 19-9 before surgery in units / ml.

4. The number of metastases in size from 2 to 3 cm.

Using the statistical computer program Statistical Package for Social Science, when executing the Wald algorithm, regression coefficients B1-B4, constant B0 were determined for each predictor and their significance levels were determined.

The parameters of the mathematical model for predicting the development of disease progression after radiofrequency thermal ablation of colorectal cancer metastases in the liver, where: B1-4 are the regression coefficients; B0 - constant with a negative sign “-”, p - level of significance of differences; expB - exponent of the regression coefficients, are presented in the table

predictors X regression coefficient B coefficient value B Statistical
what a mistake Wald statistics degree of freedom p expB number of metastases X1 B1 4,683 1,846 6,434 one 0.011 108,095 the value of the cancer-embryo
flax antigen prior to surgery ng / ml X2 B2 0,021 , 011 3,658 one 0,049 1,021 the value of cancer antigen 19-9 before surgery, units / ml X3 B3 0,032 017 3,609 one 0,043 1,032 the number of metastases in size from 2 to 3 cm X4 B4 -4,680 1,797 6,781 one 0.009 , 009 B0 -12,028 6,550 3,372 one 0,028 0,000

All 4 predictors X1-X4, which are the parameters of the mathematical model for predicting the development of disease progression after radiofrequency thermoablation of metastases of colorectal cancer in the liver, are statistically significantly different from 0, the significance is checked using Wald statistics. The number of degrees of freedom is 1 if the hypothesis of equality of 0 to the coefficient of an ordinary or index variable is tested, and for a categorical variable it is equal to the number of values without unity, the number of corresponding index variables. In the table, the coefficients of all variables were at the level of 5%, which indicates their significance. Considering the results of Wald statistics and standardized regression coefficients, the number of pre-operative metastases in the first year after radiofrequency ablation plays the most influence on the number of metastases, the second is feedback with the frequency of occurrence of average size metastases of 2-3 cm. According to the model and the obtained values of the coefficients for other fixed variables, an increase in the number of metastases per unit increases the chances of having a relapse / progression during the first year after radiofrequency ablation by 108 times, and increasing The increase in the number of metastatic formations of 2-3 cm reduces this risk by approximately 9 times. Thus, the mathematical model calculated by the formula:

P = 1/1 + e ^{- (B1 * X1 + B2 * X2 + B3 * X3 + B4 * X4-B0)} ,

because B0 is a constant with a negative sign,

as follows:

P = ^1/1 + e ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

The quality of approximation of the mathematical model to the correspondence of the mathematical model with real data is estimated using combined tests.

options X-square degrees of freedom p -2LL Cox and Snell R-Square R-square Nijelkerk indicators 94,903 four 0,000 17.15 0.64 0.913

A measure of the similarity function is the negative value of the doubled logarithm of this function - 2LL. The introduction or removal of predictors leads to a change in this function, the difference of these functions is denoted as an X-square and in our case is significant. The indicators of Cox and Snell, Nejelkerk are measures of certainty. These are pseudo-coefficients of determination, obtained on the basis of the relationship of the likelihood function of models with only a constant and with all coefficients. They indicate that part of the variance that can be determined using logistic regression. The measure of certainty according to Cox and Snell has the disadvantage that a value of 1 is theoretically unattainable. This disadvantage is eliminated due to the modification of this measure by the Neijelkerk method. After step 8, the most adequate model was obtained: the X-square criterion is 94.90 (p = 0.000), the Neijelkerk determination coefficient is 91.3%, and this percentage of the variability of the variable can be explained using the used predictors.

The following is a classification table calculated on the basis of the above model. A comparison was made of the results in 93 patients of group 1a obtained by performing computed tomography of the abdominal cavity in order to detect relapse and progression of the metastatic process (observed results) and predicted results obtained using the mathematical model. The coding of the dependent variable was carried out as follows:

and - during the first year of observation after surgery, there was no relapse or progression of the metastatic process - 0;

b - during the first year of observation after surgery, a relapse or progression of the metastatic process was revealed - 1.

Initially, the separation point, the cutoff threshold for evaluating the results obtained using the model, for the occurrence and non-occurrence of the event was 0.5. If the value turned out to be less than 0.5, then it could be assumed that the event - in our case of the development of the disease progression after radiofrequency thermal ablation of metastases of colorectal cancer in the liver - did not occur, otherwise the event was supposed to occur. The classification table for the training sample with a cutoff threshold of 0.5.

observed results predicted results 0 one 0 24 (IO) 3 (LP) one 3 (LO) 63 (IP)

Of the total number of patients, 93, on the basis of calculations, the development of progression could be predicted in 66 patients - of these, the diagnosis was confirmed by computed tomography: 63 patients had a true positive result, the remaining 3 made a false positive result. Progression for 1 year according to the results of the model was not detected in 27 observations, of which 3 patients with diagnosed metastases according to computed tomography showed a false negative result, the remaining 24 patients did not have new metastases and relapses - a truly negative result. Given the data obtained, the sensitivity, specificity and accuracy of this mathematical model were calculated at a cut-off point of 0.5.

Sensitivity = (true positive result / (true positive result + false negative result)) * 100 = 63 / (63 + 3) = 95.5%

Specificity = (true negative result / (true negative result + false positive result)) * 100 = 24 / (24 + 3) = 88.89%

Accuracy = ((true positive result + true negative result) / (true positive result + false positive result + false negative result + true negative result)) * 100 = 24 + 63/24 + 3 + 3 + 63 = 93.5%

With a cut-off point of 0.5 for the model as a whole, 93.5% of all examined and 95.5% of those who metastases really occurred during the year were correctly recognized.

To achieve maximum total sensitivity and model specificity, it is necessary to determine the optimal cutoff threshold. To determine the optimal choice of the cutoff threshold, an ROC curve was constructed and analyzed.

The computer program Statistical Package for Social Science created a table of predicted results obtained using a mathematical model that included P values for 93 patients. The range of values was from 0 to 1.

Then, using the computer program Statistical Package for Social Science, sensitivity and specificity values were calculated for each predicted value of P, which the program took as a cutoff threshold.

In the following table of coordinates for the ROC curve, bold lines are marked with a cut-off point of 0.50, which was used by the program by default, and the corresponding sensitivity and specificity, and a cut-off point of 0.69, which corresponded to the maximum sensitivity and specificity for this group the studied patients.

Predicted Values (P) Sp Se Sp + se one 2 3 four 0.00 1.00 0.00 1.00 0.00 1.00 0.04 1,04 0.00 1.00 0,07 1,07 0.00 1.00 0.11 1,11 0.00 1.00 0.15 1.15 0.00 1.00 0.19 1.19 0.00 1.00 0.22 1.22 0.00 1.00 0.26 1.26 0.00 1.00 0.30 1.30 0.00 1.00 0.33 1.33 0.00 1.00 0.37 1.37 0.00 1.00 0.41 1.41 0.00 1.00 0.44 1.44 0.00 1.00 0.48 1.48 0.00 1.00 0.52 1,52 0.00 1.00 0.56 1,56 0.01 1.00 0.59 1,59 0.01 1.00 0.63 1,63 0.01 1.00 0.67 1,67 0.02 1.00 0.70 1.70 0,03 1.00 0.74 1.74 0.10 1.00 0.78 1.78 0.20 1.00 0.81 1.81 0.23 0.98 0.81 1.80 0.29 0.97 0.81 1.78 0.40 0.97 0.85 1.82 0.48 0.95 0.85 1.81 0.50 0.95 0.89 1.84 0.55 0.95 0.93 1.88 0.62 0.95 0.96 1.92 0.69 0.95 1.00 1.95 0.77 0.94 1.00 1.94 0.87 0.92 1.00 1.92 0.92 0.91 1.00 1.91 0.93 0.89 1.00 1.89 0.95 0.88 1.00 1.88 0.98 0.86 1.00 1.86 0.98 0.85 1.00 1.85 0.99 0.83 1.00 1.83 0.99 0.82 1.00 1.82 0.99 0.80 1.00 1.80 0.99 0.79 1.00 1.79 0.99 0.77 1.00 1.77 0.99 0.76 1.00 1.76 1.00 0.74 1.00 1.74 1.00 0.73 1.00 1.73 1.00 0.71 1.00 1.71 1.00 0.70 1.00 1.70 1.00 0.68 1.00 1.68 1.00 0.67 1.00 1,67 1.00 0.65 1.00 1.65 1.00 0.64 1.00 1,64 1.00 0.62 1.00 1,62 1.00 0.61 1.00 1,61 1.00 0.59 1.00 1,59 1.00 0.58 1.00 1,58 1.00 0.56 1.00 1,56 1.00 0.55 1.00 1.55 1.00 0.53 1.00 1,53 1.00 0.52 1.00 1,52 1.00 0.50 1.00 1,50 1.00 0.48 1.00 1.48 1.00 0.47 1.00 1.47 1.00 0.45 1.00 1.45 1.00 0.44 1.00 1.44 1.00 0.42 1.00 1.42 1.00 0.41 1.00 1.41 1.00 0.39 1.00 1.39 1.00 0.38 1.00 1.38 1.00 0.35 1.00 1.35 1.00 0.33 1.00 1.33 1.00 0.32 1.00 1.32 1.00 0.30 1.00 1.30 1.00 0.29 1.00 1.29 1.00 0.27 1.00 1.27 1.00 0.26 1.00 1.26 1.00 0.24 1.00 1.24 1.00 0.23 1.00 1.23 1.00 0.21 1.00 1.21 1.00 0.20 1.00 1.20 1.00 0.18 1.00 1.18 1.00 0.17 1.00 1.17 1.00 0.15 1.00 1.15 1.00 0.14 1.00 1.14 1.00 0.12 1.00 1.12 1.00 0.11 1.00 1,11 1.00 0.09 1.00 1.09 1.00 0.08 1.00 1,08 1.00 0.06 1.00 1.06 1.00 0.05 1.00 1.05 1.00 0,03 1.00 1,03 1.00 0.00 1.00 1.00

The following is a classification table calculated on the basis of a study of 93 patients of group 1a using computed tomography of the abdominal cavity with contrast enhancement and the predicted results using a mathematical model with a cut-off point of 0.69.

observed results predicted results 0 one 0 27 (IO) 0 (LP) one 3 (LO) 63 (IP)

Given the data obtained, the sensitivity, specificity and accuracy of this mathematical model were calculated at a cut-off point of 0.69.

Sensitivity = (true positive result / (true positive result + false negative result)) * 100 = 63 / (63 + 3) * 100 = 95.5%.

Specificity = (true negative result / (true negative result + false positive result)) * 100 = 27/27 * 100 = 100%.

Accuracy = ((true positive result + true negative result) / (true positive result + false positive result + false negative result + true negative result)) * 100 = 27 + 63/27 + 0 + 3 + 63 = 96.8%.

Thus, the change in the separation point, which is reflected in the coordinate table for the ROC curve, allowed us to increase the specificity to 100% while maintaining the sensitivity level.

To finally determine the quality of the mathematical model according to the coordinate table for the ROC curve, a ROC curve was constructed, which is shown in Figure 1, sensitivity values - Se are plotted along the ordinate axis, and Sp = 100% specificity is plotted along the abscissa axis.

In an “ideal test”, the curve passes through the upper left corner, where the share of truly positive cases is 100%, respectively, the lower the curve bend, the less quality the test. The graph is supplemented by the straight line y = x, since it is impractical to consider the ROC curve below the straight line y = x. To obtain the numerical value of the clinical significance of the test, the area under the ROC curve and area under curve - AUC indicators are used. You can judge the quality of the test by the expert scale for the area under curve values:

0.9-1.0 - excellent;

0.8-0.9 - very good;

0.7-0.8 - good;

0.6-0.7 - average;

0.5-0.6 - unsatisfactory.

The area under curve for the resulting model was 0.99 ± 0.006 (95% CI: 0.98-1.00), which corresponds to excellent test quality on an expert scale for area under curve values.

The proposed method is as follows: the patient before performing radiofrequency ablation perform computed tomography or magnetic resonance imaging of the liver, in which they determine: the number of metastases of colorectal cancer and their size. Then, the level of cancer-embryonic antigen and the tumor marker of cancer antigen 19-9 are determined in venous blood. Substitute the obtained data in the formula:

P = ^1/1 + 2.71 ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

where P is the probability that a disease progression will occur after radiofrequency thermal ablation of colorectal cancer metastases to the liver in fractions of units; X1 - the number of metastases of colorectal cancer; X2 - preoperative value of cancer-embryonic antigen; X3 - preoperative value of cancer antigen 19-9; X4 - the number of colorectal cancer metastases in size from 2 to 3 cm; and when a P value less than or equal to 0.69 is obtained, the risk of developing a process progression is low, and when a P value is greater than 0.69, the risk of developing a process progression is high.

Practical verification of the quality of our chosen model:

the calculation was based on a retrospective analysis of medical records. To confirm the effectiveness of the proposed method, based on the data obtained by the program Statistical Package for Social Science, a table of predicted results obtained using a mathematical model was created, which included P values for 93 patients. The range of values was from 0 to 1.

Then, the values of sensitivity and specificity were calculated for each predicted value P of positive and negative prognostic significance.

The method we developed allows us to combine and order the above factors for the progression of the disease after performing radiofrequency thermoablation of metastases of colorectal cancer in the liver, both laboratory parameters, and computed tomography and magnetic resonance imaging data as a whole, also simplifies and systematizes approaches to the analysis of the obtained data, which allows us to draw up the correct plan treatment.

Clinical example No. 1

Patient Ch. 59 years old was admitted to hospital with a diagnosis of T4N1M0 cecum cancer III stage (hemicolectomy on the right in 2010). The progression of the process is single metastases in the right lobe of the liver. The patient undergoes computed tomography of the abdominal cavity, the number of metastatic formations and their sizes are determined. Blood sampling is performed for tumor markers: cancer embryonic antigen and cancer antigen 19-9. According to the survey revealed: two metastatic formations in 6 and 7 segments of the liver, sizes 21 and 25 mm. Blood data of cancer antigen 19-9 = 80 units / ml, blood test data for cancer-embryonic antigen = 9.1 ng / m. Substitute the obtained data in the formula:

P = ^1/1 + 2.71 ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

X1 - the number of metastases of colorectal cancer, in our patient = 2.

X2 - preoperative value of cancer-embryonic antigen, in our patient = 9.1 ng / ml.

X3 - preoperative value of cancer antigen 19-9, in our patient = 80 units / ml.

X4 - the number of colorectal cancer metastases in size from 2 to 3 cm, in our patient = 2.

The value of 2.71 is the basis of natural logarithms.

The result is less than 0.69, which corresponds to a low risk of progression of the disease.

The patient was treated: radiofrequency ablation of liver metastases followed by four courses of chemotherapy, after 6 months during the next clinical examination, data on the progression of the disease were not revealed, which confirms the reliability of the calculations.

Example 2

Patient M., 62 years old, was admitted to hospital for treatment at the abdominal oncology department with a diagnosis of Cancer of the sigmoid colon T4N1M0 (resection of the sigmoid colon in 2010). Progression of the process: liver metastases. The patient underwent examination: computed tomography of the abdominal cavity, the number of metastatic formations and their sizes are determined. Blood sampling is performed on tumor markers: cancer-embryonic antigen and cancer antigen 19-9. The results are obtained: according to computed tomography of the abdominal cavity of both lobes of the liver, six metastatic formations were found, of which three are 28 mm in size. Blood data of cancer antigen 19-9 = 250 units / ml, blood test data for cancer-embryonic antigen = 21.1 ng / m.

P = ^1/1 + 2.71 ^{- (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}

X1 - the number of metastases of colorectal cancer, in our patient = 6.

X2 - preoperative value of cancer-embryonic antigen, in our patient = 21.1 ng / ml.

X3 - preoperative value of cancer antigen 19-9, in our patient = 250 u / ml.

X4 is the number of colorectal cancer metastases from 2 to 3 cm in size, in our patient = 3.

The value of 2.71 is the basis of natural logarithms.

A result of more than 0.69 was obtained, which corresponds to a high risk of developing a disease progression during the first year.

The patient underwent treatment: an operation — radiofrequency ablation of liver metastases followed by four courses of chemotherapy, after 6 months, during the next clinical examination, computed tomography of the liver was performed, in which two additional tumor foci were detected in the second and third segments of the liver, which confirms the reliability of the calculations .

The method is intended to predict the development of the progression of the process during the first year. And can be used in oncological units of medical institutions.

Information sources

1. Kosyrev V.Yu. Radiofrequency thermal ablation in the treatment of patients with hepatocellular cancer and metastases of colorectal cancer in the liver. Literature review [Text] / V.Yu. Kosyrev, B.I. Dolgushin // Medical radiology and radiation safety. - 2011. - No. 2. - S.68-81.

2. Radiation diagnostic methods in assessing changes in the area of radiofrequency thermal ablation of liver tumors [Text] / B.I. Dolgushin [et al.] // Vestnik RONTs im. N.N. Blokhina RAMS. - 2008. - No. 2. - S. 35-42.

3. Kosyrev V.Yu. Radiofrequency thermal ablation in the combined treatment of malignant liver tumors (indications, methodology, treatment results) [Text]: author. dis ... doctor honey. sciences / V.Yu. Kosyrev. - M., 2011 .-- 42 p.

4. Adequate assessment of the effectiveness of magnetic resonance imaging during radiofrequency thermal ablation in patients with malignant liver tumors [Text] / GG Karmazanovsky [et al.] // Metastatic liver cancer: materials of the plenum of the Board of the Intern. Societies. org "Association of Hepatologist Surgeons." - 2010 .-- P.57-58.

5. Vilyavin M.Yu. Diagnosis of postoperative complications and evaluation of the results of operations on the liver using computed tomography [Text] / M.Yu. Vilyavin // Materials of the XVII International Congress of surgeons-hepatologists of the CIS countries: “Actual problems of surgical hepatology. - 2010 .-- S.16-17.

6. Islamov X.D. Prediction of treatment outcomes for patients with colorectal cancer with multiple liver metastases [Text] / X. D. Islamov, S. N. Navruzov, S. B. Abduzhabbarov, M. S. Gildieva // Russian Oncological Journal. - 2010. - No. 5. - S. 37-39.

7. Tumor Marker Evolution: Comparison with Imaging for Assessment of Response to Chemotherapy in Patients with Colorectal Liver Metastases [Text] / R.J.de Haas [et al] // Ann Surg. Oncol. - 2010. - No. 17. - P.1010-1023.

Claims (1)

The method of preoperative determination of the effectiveness of performing radiofrequency thermoablation of colorectal cancer metastases in the liver by analyzing preoperative data of computed tomography of the liver consists in taking into account the number and size of tumor foci, characterized in that prior to the operation, the level of cancer-embryonic antigen and cancer antigen 19-9 blood and calculate the probability of the progression of disease P by the formula:
P = ^{1/1 + 2/71 - (4.68 * X1 + 0.02 * X2 + 0.03 * X3-4.68 * X4-12.03)}
where P is the probability that a disease progression will occur after radiofrequency thermal ablation of colorectal cancer metastases to the liver in fractions of units; X1 - the number of metastases of colorectal cancer; X2 - preoperative value of cancer-embryonic antigen; X3 - preoperative value of cancer antigen 19-9; X4 - the number of colorectal cancer metastases in size from 2 to 3 cm; and when P is less than or equal to 0.69, the risk of developing a process progression is low, and when P is greater than 0.69, the risk of developing a process progression is high.

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