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EP-4736110-A1 - METHOD FOR CLASSIFYING A LESION AS A UTERINE LEIOMYOMA OR AS A UTERINE LEIOMYOSARCOMA

EP4736110A1EP 4736110 A1EP4736110 A1EP 4736110A1EP-4736110-A1

Abstract

A computer-implemented method (100) for classifying a lesion as a uterine leiomyoma, UM, or as a uterine leiomyosarcoma, LMS, the method (100) comprising: acquiring (101) at least one Computed Tomography, CT, image of the lesion; obtaining (103) a Region of Interest, ROI, of the lesion within the at least one CT image; calculating (105) one or more radiomic features of said ROI of the lesion; selecting (107), using a plurality of feature se-lection functions, a corresponding plurality of sub-sets of features, wherein each of said plurality of subsets of features comprises one or more selected radiomic features of said one or more radiomic features; applying (109) a corresponding plurality of predictive models which have been trained for classifying a lesion as UM or as LMS to the plurality of subsets of features; and classifying (113) the lesion as UM or as LMS using said plurality of the predictive models.

Inventors

  • STRIGARI, Lidia
  • SANTORO, Miriam
  • PAOLANI, Giulia
  • PERRONE, Anna Myriam
  • DE IACO, Pierandrea
  • COADA, Camelia Alexandra

Assignees

  • Alma Mater Studiorum - Università di Bologna
  • IRCCS Azienda Ospedaliero - Universitaria di Bologna

Dates

Publication Date
20260506
Application Date
20240617

Claims (18)

  1. 1. A computer-implemented method (100) for classifying a lesion as a uterine leiomyoma, UM, or as a uterine leiomyosarcoma, LMS, the method (100) comprising: acquiring (101 ) at least one Computed Tomography, CT, image of the lesion; obtaining (103) a Region of Interest, ROI, of the lesion within the at least one CT image; calculating (105) one or more radiomic features of said ROI of the lesion; selecting (107), using a plurality of feature selection functions, a corresponding plurality of subsets of features, wherein each of said plurality of subsets of features comprises one or more selected radiomic features of said one or more radiomic features; applying (109) a corresponding plurality of predictive models which have been trained for classifying a lesion as UM or as LMS to the plurality of subsets of features; and classifying (113) the lesion as UM or as LMS using said plurality of the predictive models.
  2. 2. The method according to claim 1 , wherein acquiring (101 ) at least one CT image of the lesion comprises acquiring at least one contrast-enhanced computed tomography, CE-CT, image.
  3. 3. The method according to claim 1 or 2, further comprising processing (102) the at least one CT image to obtain at least one processed CT image having a preset isotropic voxel spacing and a pre-set voxel density; and wherein the obtaining (103) the ROI of the lesion is based on the least one processed CT image.
  4. 4. The method according to any one of the preceding claims, wherein said plurality of feature selection functions comprises a random forest optimizer function, a regression analysis function, and a recursive feature elimination function.
  5. 5. The method according to any one of the preceding claims, wherein said plurality of predictive models comprise machine learning-based models that have been trained and tested for the binary classification task based on results of a histological/histopathological examination of UM or LMS.
  6. 6. The method of any one of the preceding claims, wherein classifying (113) the lesion comprises: obtaining a probability that the lesion corresponds to UM or LMS based on the predictive model; and classifying the lesion as UM or LMS in response to a determination that said probability reaches a probability threshold value.
  7. 7. The method of any one of the preceding claims, further comprising performing a cross-correlation analysis (115) of the classification outputs obtained by using said plurality of predictive models; and determining (116) that the lesion is UM or LMS based on the cross-correlation analysis.
  8. 8. A computer-implemented method of training a system for classifying a lesion as a uterine leiomyoma, UM, or as a uterine leiomyosarcoma, LMS, the method (200) comprising: acquiring (201 ) a set of Computed Tomography, CT, images of lesions; obtaining (203) a Region of Interest, ROI, of the lesion within each of the set of CT images; acquiring (204) a set of labels, each being indicative that the corresponding ROI comprises a UM or a LMS; calculating (205) one or more radiomic features of said ROI of the lesion; selecting (207), using a plurality of feature selection functions, a corresponding plurality of subsets of features, wherein each of said plurality of subsets of features comprises one or more selected radiomic features of said one or more radiomic features; generating (209), based on the plurality of subsets of radiomic features, a plurality of predictive models that are trained for classifying a lesion as UM or as LMS based on the set of labels; and performing (211 ) a number of cross-validation cycles of the plurality of predictive models.
  9. 9. The method according to claim 8, wherein generating (209) a plurality of predictive models comprises combining the selected one or more radiomic features of each of the plurality of subset of radiomic features to fit a predictive model of said plurality of predictive models.
  10. 10. The method according to claim 9, wherein said predictive model is a generalized linear model.
  11. 11. The method according to any one of claims 8-10, wherein performing (211 ) a number of cross-validation cycles comprises, for each cycle and for each predictive model: adjusting one or more parameter values used to control the learning process of the predictive model; and estimating the predictive power score for classifying the lesion as UM or as LMS of the predictive model.
  12. 12. The method according to any one of claims 8-11 , wherein performing (211 ) a number of cross-validation cycles comprises, for each cycle and for each predictive model splitting the set of CT images into a training subset and test subset with balanced output.
  13. 13. The method according to any one of claims 8-12, further comprising: performing (213) an evaluation cycle of the predictive models; and selecting (215) a predictive model for performing classification based on a result of the evaluation cycle.
  14. 14. The method according to claim 13, wherein performing (213) an evaluation cycle comprises, for each of said predictive models: calculating an area, AUC, below a receiver operational characteristic curve, ROC, associated with the predictive model; and estimating the predictive power score of the predictive model based on said AUC.
  15. 15. The method according to claim 13, wherein selecting (215) a predictive model for performing classification comprises selecting (215) a predictive model having the predictive power score that reaches a predictive power threshold.
  16. 16. A system (1 ) comprising: a memory (10) for storage of at least one Computed Tomography, CT, image of a lesion; and a processing unit (20) communicatively coupled to the memory (10) and configured to execute the method of any one of the preceding claims.
  17. 17. A computer program product comprising one or more instructions that, when executed by a processing unit (20), cause a system (1 ) to carry out the method of any one of claims 1 -7 or 8-15.
  18. 18. A computer-readable storage medium comprising instructions which, when executed by a processing unit (20), cause a system (1 ) to carry out the method of any one of claims 1 -7 or 8-15.

Description

Method for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma Description TECHNICAL FIELD [0001]The present disclosure concerns a computer-implemented method for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma, which can accurately discriminate between uterine leiomyoma or uterine leiomyosarcoma. [0002]The present disclosure also concerns a computer implemented method of training a system for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma. [0003]The subject matter disclosed herein also refers to a system, a computer program product and a computer-readable storage medium for performing classification and/or training. BACKGROUND ART [0004] Uterine leiomyosarcomas (LMS) are rare tumours arising from the muscular uterine wall, which represent 3-7% of uterine malignancies and around 1 % of all female genital tract cancers [1 ], Compared with other types of uterine cancers, they are aggressive tumours with a high risk of recurrence and death, regardless of the stage of the disease at diagnosis [1], Surgical treatment in the early stage consists of total removal of the uterus (hysterectomy) by laparotomy to avoid the high risk of neoplastic spread through rupture and fragmentation of the tumour [2], [0005] On the other hand, uterine leiomyoma (UM) represents a benign pathology also arising from the muscular wall of the uterus, but very frequent, with an incidence of about 70%-80% [3], [0006] Discrimination between benign and malignant myometrial lesions is clinically important for planning optimal management (hysterectomy in LMSs, fertility-sparing surgery, medical treatment, or no treatment in UMs) and defining the most appropriate and personalized surgical approach (laparotomy in LMSs versus minimally invasive surgery in UMs) [3,4], [0007] The accurate and pre-operative diagnosis of LMS is important because of the growing availability of more conservative approaches to managing benign uterine masses [5], In contrast, a misdiagnosis can severely impact patient's prognosis, as a morcellated LMSs increases the risk of dissemination of neoplastic material within the abdomen and the possibility of creating an iatrogenic abdominal sarcomatosis [6-8], [0008] Unfortunately, the diagnosis of LMS is always defined post operation with the definitive histological examination. [0009] Considering the above and the lack of methods for accurately diagnose LMSs and UMs pre-operation, there is an unmet clinical need to provide an accurate method for correctly diagnose LMSs and UMs without the necessity of an intraoperative procedure, which is invasive and can increase the risk of dissemination of neoplastic material. SUMMARY The present application provides a method designed to classify a lesion as LMS or as UM, using computer tomography images. This method achieves optimal level of accuracy in the differential diagnosis of these tumours. The present application also provides a computer implemented method of training a system for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma. The invention therefore covers a classification method as claimed in claim 1 , a training method as claimed in claim 8, a system as claimed in claim 16, a computer program product as claimed in claim 17 and a computer-readable storage medium as claimed in claim 18. Further, preferred advantageous embodiments are described in the dependent claims. BRIEF DESCRIPTION OF THE DRAWINGS [0010] A more complete appreciation of the disclosed embodiments of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein: Figure 1 shows a schematic view of a system for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma, according to the present disclosure. Figure 2 illustrates a flowchart of a method for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma, according to the present disclosure. Figure 3 illustrates a flowchart of a method of training a system, such as the system shown in figure 1 , for classifying a lesion as a uterine leiomyoma or as a uterine leiomyosarcoma, according to the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS [0011] Reference is made to figure 1 , which shows a schematic view of a system 1 for classifying a lesion, such as a mesenchymal lesion of the uterus, as a uterine leiomyoma (UM) or as a uterine leiomyosarcoma (LMS). The system 1 can also be trained for classifying a lesion as UM or LMS as described with reference to figure 3. [0012]The system 1 comprises a memory 10 and a processor 20. The system 1 may also comprise a display 30 for displaying the classification output generated by the processing unit 20 [0013]The processing unit 20 is communicatively coupled to the memory 10 and configured to