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An Intelligent System for Diagnostics of Pancreatic Diseases

Abstract

This paper describes an intelligent system that performs the JSM automated research support method, which is designed to diagnose pancreatic diseases, that is, chronic pancreatitis and pancreatic cancer. A preliminary study is presented; further trends for the development of the system are listed.

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REFERENCES

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    Shesternikova, O.P. and Pankratova, E.S., An intelligent system for detecting patterns in gastroenterological data, Trudy Pyatnadtsatoi natsional’noi konferentsii po iskusstvennomu intellektu s mezhdunarodnym uchastiem KII-2016 (3–7 oktyabrya 2016 g., g. Smolensk, Rossiya) (Proc. Fifteenth National Conference on Artificial Intelligence with International Participation KII-2016 (October 3–7, 2016, Smolensk, Russia)), Smolensk, 2016, vol. 1, pp. 396–404.

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    Finn, V.K. and Shesternikova, O.P., The heuristics of detection of empirical regularities by JSM reasoning, Autom. Doc. Math. Linguist., 2018, vol. 52, no. 5, pp. 215–247.

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    DSM-metod avtomaticheskogo porozhdeniya gipotez: Logicheskie i epistemologicheskie osnovaniya, (The JSM Method for Automatic Hypothesis Generation: Logical and Epistemological Foundations), Anshakov, O.M., Ed., Moscow: LIBROKOM, 2009.

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    Russo, A., Rosell, R., and Rolfo, C., Targeted Therapies for Solid Tumors: A Handbook for Moving Toward New Frontiers in Cancer Treatment, Humana Press, 2015.

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    Pinho, A.V., Chantrill, L., and Rooman, I., Chronic pancreatitis: A path to pancreatic cancer, Cancer Lett., 2013, vol. 345, no. 2, pp. 203–209.

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FUNDING

The study was performed with partial support of the Russian Foundation for Basic Research (project no. 18-29-03063MK).

Author information

Correspondence to O. P. Shesternikova or V. K. Finn or L. V. Vinokurova or K. A. Les’ko or G. G. Varvanina or E. Yu. Tyulyaeva.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by L. Solovyova

Appendices

APPENDIX 1

The list of features used in the fact base and their data types

No. Sign name Data type
  1. Clinical data  
1 1.1 Gender Enumeration
2 1.2 Age Integer
3 1.3 Body mass index Number with two decimal digits
4 1.4 Duration of the disease Number with two decimal digits
5 1.5 Presence of alcohol addiction Binary type
6 1.6 Availability of tobacco addiction Binary type
7 1.7 Development of diabetes Binary type
8 1.8 Pancreatic cancer Binary type
  2. Laboratory data  
  2.1 Biochemistry  
9 2.1.1 Total bilirubin Number with two decimal digits
10 2.1.2 Direct bilirubin Number with two decimal digits
11 2.1.3 Indirect bilirubin Number with two decimal digits
12 2.1.4 Gamma-glutamyltranspeptidase (GGTP) Number with two decimal digits
13 2.1.6 Glucose Number with two decimal digits
14 2.1.5 Total protein Number with two decimal digits
15 2.1.7 c-peptide Number with two decimal digits
16 2.1.8 Fecal elastase Number with two decimal digits
  2.2 General blood test  
17 2.2.1 Hemoglobin Number with two decimal digits
18 2.2.2 White blood cells Number with two decimal digits
19 2.2.3 ESR Number with two decimal digits
  2.3 Oncomarkers  
20 2.3.1 CA 19-9 Number with two decimal digits
21 2.3.2 CA 242 Number with two decimal digits
22 2.3.3 CEA Number with two decimal digits
  3. Ultrasound examination  
  3.1 Reliable signs of pancreatic cancer (PC)  
23 3.1.1 Identification of a volumetric neoplasm (more often solid), hypo and isoechoic identification Binary type
  3.2 Indirect signs of PC  
24 3.2.1 Uniform dilatation of the main pancreatic duct (MPD) without pronounced wall compaction Binary type
  3.3 Direct signs of chronic pancreatitis  
25 3.3.1 Calcifications Binary type
  3.4 Signs of chronic pancreatitis  
26 3.4.1 Hyperechoic structure of the pancreas Binary type
27 3.4.2 Uneven dilatation of the MPD, compaction of its walls Binary type
  4. Computed tomography (CT)  
28 4.1 Neoplasms in the structure of the pancreas Binary type
29 4.2 Biliary hypertension Binary type
  4.3 Dilatation of the MPD  
30 4.3.1 No Binary type
31 4.3.2 Yes, regular Binary type
32 4.3.3 Yes, irregular Binary type
  4.4 Densitometric characteristics for pancreatic cancer in phases, HU  
  4.4.1 Native  
33 4.4.1.1 min Integer
34 4.4.1.1 max Integer
  4.4.2 Arterial  
35 4.4.2.1 min Integer
36 4.4.2.1 max Integer
  4.4.3 Venous  
37 4.4.3.1 min Integer
38 4.4.3.1 max Integer
  4.4.4 Delayed  
39 4.4.4.1 min Integer
40 4.4.4.1 max Integer
  4.5 Density gradient between tumor and unchanged tissue  
  4.5.1 Native  
41 4.5.1.1 min Integer
42 4.5.1.1 max Integer
  4.5.2 Arterial  
43 4.5.2.1 min Integer
44 4.5.2.1 max Integer
  4.5.3 Venous  
45 4.5.3.1 min Integer
46 4.5.3.1 max Integer
  4.5.4 Delayed  
47 4.5.4.1 min Integer
48 4.5.4.1 max Integer
  4.6 Gradient average  
49 4.6.1 Native Number with two decimal digits
50 4.6.2 Arterial Number with two decimal digits
51 4.6.3 Venous Number with two decimal digits
52 4.6.4 Delayed Number with two decimal digits

An example of a correct prediction

The source example for prediction:

0—Id 73
1—Gender M
2—Age 72
3—BMI 26.4
4—Duration of the disease 4
5—Alcohol Yes
6—Smoking Yes
7—ID No
8—Pancreatic cancer  
9—Total bilirubin 12.9
10—Direct bilirubin 2.9
11– Indirect bilirubin 10
12– GGTP 202
13—Total protein 70.7
14—Glucose 5.9
15—C-peptide 0.6
16—Fecal elastase 296
17—Hemoglobin 127
18—White blood cells 6.3
19—ESR 33
20—CaA19-9 974
21—CA 242 150
22—CEA 4.5
23—Detection of a voluminous neoplasm (more often solid), hypo and isoechoic detection Yes
24—(US) Regular dilatation of the MPD without pronounced compaction of its walls Yes
25—(US) Regular dilatation of the MPD without pronounced compaction of its walls No
26—Hyperechoic structure of the pancreas Yes
27—(US) Irregular dilatation of the MPD, compaction of its walls No
28—Neoplasms in the structure of the pancreas Yes
29—Biliary hypertension Yes
30—(CT) There is a dilatation of the MPD No
31—(CT) Regular dilatation of the MPD Yes
32—(CT) Irregular dilatation of the MPD No
33—Densitometry (native, min) 20
34—Densitometry (native, max) 77
35—Densitometry (arterial, min) 16
36—Densitometry (arterial, max) 106
37—Densitometry (venous, min) 24
38—Densitometry (venous, max) 121
39—Densitometry (delayed, min) 37
40—Densitometry (delayed, max) 137
41– Gradient (native, min) 24
42—Gradient (native, max) 8
43—Gradient (arterial, min) 8
44—Gradient (arterial, max) 6
45—Gradient (venous, min) 43
46—Gradient (venous, max) 23
47—Gradient (delayed, min) 22
48—Gradient (delayed, max) 29
49—Gradient average (native) 16
50—Gradient average (arterial) 7
51—Gradient average (venous) 33
52—Gradient average (delayed) 25.5
In this example, the system diagnosed PC using the following hypotheses (the signs that have no values in the hypothesis are omitted):
Hypothesis 1
22—CEA 3.1–10.2
25—(US) Regular dilatation of the MPD without pronounced compaction of walls No
26—Hyperechoic structure of the gland Yes
28—Neoplasms in the structure of the pancreas Yes
49—Gradient average (native) 9–16
Hypothesis 2
4—Duration of the disease 0.45–4
14—Glucose 5.13–6.27
23—Detection of a voluminous neoplasm (more often solid), hypo and isoechoic detection Yes
25—US) Regular dilatation of the MPD without pronounced compaction of walls No
26—Hyperechoic structure of the gland Yes

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Shesternikova, O.P., Finn, V.K., Vinokurova, L.V. et al. An Intelligent System for Diagnostics of Pancreatic Diseases. Autom. Doc. Math. Linguist. 53, 288–294 (2019) doi:10.3103/S000510551905008X

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Keywords:

  • JSM ARS method
  • intelligent system
  • pancreatic cancer
  • chronic pancreatitis