SCIENCE CHINA Information Sciences, Volume 62 , Issue 11 : 212205(2019) https://doi.org/10.1007/s11432-019-9866-3

Data-driven group decision making for diagnosis of thyroid nodule

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  • ReceivedJan 2, 2019
  • AcceptedMar 29, 2019
  • PublishedSep 20, 2019


Emerging information technologies' integration into various fields has enhanced the development of these fields. Large volumes of data have been accumulated in this process. The accumulated data offer opportunities and challenges for people facing practical problems. On the one hand, it is essential to depend on a group's capabilities rather than an individual's capabilities to handle practical problems because the individual may lack sufficient expertise and experience to use data. In this situation, the practical problems can be considered as group decision making (GDM) problems. On the other hand, the accumulated data can help generate quality solutions to GDM problems. To obtain such solutions under the assumption that the accumulated data regarding a specific decision problem are available, this paper proposes a data-driven GDM method. In the method, decision makers' weights are learned from historical overall assessments and the corresponding gold standards, while criterion weights are learned from historical overall assessments and the corresponding decision matrices. The learned expert weights and criterion weights are used to produce the aggregated assessments, from which alternatives are compared or the overall conclusion is made. In a tertiary hospital located in Hefei, Anhui Province, China, the proposed method is applied to aid radiologists in diagnosing thyroid nodules.


This work was supported by National Natural Science Foundation of China (Grant Nos. 71622003, 71571060, 71690235, 71690230, 71521001).




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  • Figure 1

    (Color online) MCGDM process for the proposed method.

  • Figure 2

    (Color online) Movement of $\widetilde{C}_{k}^{G_{w},G_{d}}(k~=~1,\ldots,5)$ with random $\lambda_{j}(j~=~1,2,3)$. (a) $\widetilde{t}_{1}$; (b) $\widetilde{t}_{2}$; (c) $\widetilde{t}_{3}$; (d) $\widetilde{t}_{4}$; (e) $\widetilde{t}_{5}$.

  • Figure 3

    (Color online) Comparison between $\widetilde{C}_{k}^{\bar{G}_{w},G_{d}}$ and $\widetilde{C}_{k}^{G_{w},\bar{G}_{d}}(k~=~1,\ldots,5)$. (a) $\widetilde{t}_{1}$; (b) $\widetilde{t}_{2}$; (c) $\widetilde{t}_{3}$; (d) $\widetilde{t}_{4}$; (e) $\widetilde{t}_{5}$.

  • Figure 4

    Comparison between ROC curves from the group-recommended TIRADS categories and that from the overall diagnose for radiologists. (a) $\widetilde{t}_{1}$; (b) $\widetilde{t}_{2}$; (c) $\widetilde{t}_{3}$; (d) $\widetilde{t}_{4}$; (e) $\widetilde{t}_{5}$.

  • Table 1   TIRADS categories applied in the hospital
    Category Finding Cancer risk Recommendation
    TIRADS 3 Probably benign $<$3% Follow-up/FNAB
    TIRADS 4 Suspicious 3%–75%
    TIRADS 4A Low suspicion 3%–24%
    TIRADS 4A-1 Tending towards benign nodule 3%–15% Follow-up/FNAB
    TIRADS 4A-2 Not excluding the possibility of malignant nodule 16%–24% FNAB
    TIRADS 4B Intermediate suspicion 25%–75%
    TIRADS 4B-1 Not excluding the possibility of benign nodule 25%–40% FNAB
    TIRADS 4B-2 Medium possibility of malignant nodule 41%–65% FNAB
    TIRADS 4B-3 Large possibility of malignant nodule 66%–75% FNAB
    TIRADS 4C High suspicion 76%–95% FNAB
    TIRADS 5 Suggestive of malignancy $>$ 95% FNAB
  • Table 2   Details about the eight radiologists
    Radiologist Serving period Diagnostic record
    $D_{1}$ 2013–2018 591
    $D_{2}$ 2011–2018 586
    $D_{3}$ 2012–2018 628
    $D_{4}$ 2015–2018 397
    $D_{5}$ 2013–2017 179
    $D_{6}$ 2011–2016 180
    $D_{7}$ 2017–2018 202
    $D_{8}$ 2018–2018 93
  • Table 3   Weights of the five criteria for the eight radiologists
    Radiologist Learned criterion weight
    $t_{1}$ $w_{i}^{1}~~(i~=~1,\ldots,5)~=~(0.1893,~0.2386,~0.1798,~0.2119,~0.1804)$
    $t_{2}$ $w_{i}^{2}~~(i~=~1,\ldots,5)~=~(0.1901,~0.2412,~0.1778,~0.2164,~0.1745)$
    $t_{3}$ $w_{i}^{3}~~(i~=~1,\ldots,5)~=~(0.2005,~0.2352,~0.188,~0.1938,~0.1825)$
    $\widetilde{t}_{1}$ $\widetilde{w}_{i}^{1}~~(i~=~1,\ldots,5)~=~(0.2002,~0.2287,~0.1745,~0.2115,~0.1851)$
    $\widetilde{t}_{2}$ $\widetilde{w}_{i}^{2}~~(i~=~1,\ldots,5)~=~(0.1928,~0.2238,~0.1852,~0.216,~0.1822)$
    $\widetilde{t}_{3}$ $\widetilde{w}_{i}^{3}~~(i~=~1,\ldots,5)~=~(0.1981,~0.2205,~0.2126,~0.1911,~0.1778)$
    $\widetilde{t}_{4}$ $\widetilde{w}_{i}^{4}~~(i~=~1,\ldots,5)~=~(0.2027,~0.2314,~0.1668,~0.2291,~0.1699)$
    $\widetilde{t}_{5}$ $\widetilde{w}_{i}^{5}~~(i~=~1,\ldots,5)~=~(0.1811,~0.251,~0.1713,~0.2276,~0.169)$
  • Table 4   Distributions of the overall diagnoses on the TIRADS categories for the eight radiologists
    Radiologist Nodule Distributions of overall diagnoses on $T_{c}$
    $t_{1}$ Malignant $d_{1,c}^{m}~~(c~=~1,\ldots,~8)~=~(10,~14,~15,~25,~42,~25,~36,~66)$
    $t_{1}$ Benign $d_{1,c}^{b}~~(c~=~1,\ldots,~8)~=(261,~29,~15,~10,~24,~12,~2,~5)$
    $t_{2}$ Malignant $d_{2,c}^{m}~~(c~=~1,\ldots,~8)~=~(10,~1,~19,~1,~17,~49,~26,~84)$
    $t_{2}$ Benign $d_{2,c}^{b}~~(c~=~1,\ldots,~8)~=~(254,~12,~33,~11,~20,~33,~8,~8)$
    $t_{3}$ Malignant $d_{3,c}^{m}~(c~=~1,\ldots,~8)~=~(5,~1,~18,~0,~32,~76,~17,~18)$
    $t_{3}$ Benign $d_{3,c}^{b}~(c~=~1,\ldots,~8)~=~(155,~18,~23,~7,~13,~10,~2,~2)$
    $\widetilde{t}_{1}$ Malignant $\widetilde{d}_{1,c}^{m}~(c~=~1,\ldots,~8)~=~(17,~1,~21,~2,~36,~71,~52,~42)$
    $\widetilde{t}_{1}$ Benign $\widetilde{d}_{1,c}^{b}~(c~=~1,\ldots,~8)~=~(206,~31,~47,~26,~42,~20,~9,~5)$
    $\widetilde{t}_{2}$ Malignant $\widetilde{d}_{2,c}^{m}~(c~=~1,\ldots,~8)~=~(9,~3,~5,~0,~8,~14,~8,~9)$
    $\widetilde{t}_{2}$ Benign $\widetilde{d}_{2,c}^{b}~(c~=~1,\ldots,~8)~=~(66,~11,~11,~11,~13,~9,~2,~0)$
    $\widetilde{t}_{3}$ Malignant $\widetilde{d}_{3,c}^{m}~(c~=~1,\ldots,~8)~=~(3,~1,~3,~2,~5,~9,~2,~12)$
    $\widetilde{t}_{3}$ Benign $\widetilde{d}_{3,c}^{b}~~(c~=~1,\ldots,~8)~=~(84,~2,~7,~9,~20,~16,~2,~3)$
    $\widetilde{t}_{4}$ Malignant $\widetilde{d}_{4,c}^{m}~(c~=~1,\ldots,~8)~=~(3,~5,~14,~1,~14,~46,~19,~14)$
    $\widetilde{t}_{4}$ Benign $\widetilde{d}_{4,c}^{b}~(c~=~1,\ldots,~8)~=~(60,~7,~12,~1,~1,~4,~1,~0)$
    $\widetilde{t}_{5}$ Malignant $\widetilde{d}_{5,c}^{m}~(c~=~1,\ldots,~8)~=~(3,~2,~2,~2,~11,~27,~10,~2)$
    $\widetilde{t}_{5}$ Benign $\widetilde{d}_{5,c}^{b}~(c~=~1,\ldots,~8)~=~(17,~7,~4,~1,~3,~2,~0,~0)$
  • Table 5   Diagnostic capabilities of radiologists $\widetilde{t}_{k}~(k~=~1,\ldots,~5)$ in different situations
    Condition Diagnostic capabilities of five radiologists
    Group's weights and group's distributions $\widetilde{C}_{k}^{G_{w},G_{d}}~(k~=~1,\ldots,5)~=~(0.8088,~0.7712,~0.8104,~0.7793,~0.7721)$
    Group's weights and radiologists' distributions $\widetilde{C}_{k}^{G_{w},R_{d}}~(k~=~1,\ldots,5)~=(0.7566,~0.7469,~0.8104,~0.6821,~0.6577)$
    Radiologists' weights and group's distributions$\widetilde{C}_{k}^{R_{w},G_{d}}~(k~=~1,\ldots,5)~=(0.8012,~0.7712,~0.805,~0.7698,~0.7721)$
    Radiologists' weights and radiologists' distributions $\widetilde{C}_{k}^{R_{w},R_{d}}~(k~=~1,\ldots,5)~=~(0.7514,~0.7469,~0.805,~0.6745,~0.6577)$
    Radiologists' overall diagnoses $\widetilde{C}_{k}^{R_{0}}~(k~=~1,\ldots,5)~=(0.7505,~0.7317,~0.7451,~0.7445,~0.7025)$

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