Radiomics in Gynaecological Imaging: History
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Contributor:
Paolo Niccolò Franco
,
Federica Vernuccio
,
Cesare Maino
,
Roberto Cannella
,
Milagros Otero-García
,
Davide Ippolito

Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. A number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. 

  • radiomics
  • gynaecological imaging
  • MRI
  • CT
  • endometrial cancer
  • cervical cancer
  • ovarian cancer
  • mesenchymal tumours

1. Introduction

Nowadays, cross-sectional imaging is widely used in clinical practice for diagnosis, treatment planning, and monitoring of various diseases and conditions, as in the case of gynaecological pathologies, both benign and malignant [1]. One of the most used cross-sectional imaging techniques is computed tomography (CT), particularly useful for evaluating different anatomical structures and organs. CT has low accuracy for the characterization of pelvic masses, but it is helpful to evaluate the presence of secondary lesions to the thorax and abdominal organs [2][3].
The most commonly and widely used cross-sectional imaging modality to characterize and correctly stage gynaecological conditions is magnetic resonance imaging (MRI). Thanks to the high soft tissues’ spatial and contrast resolutions, MRI can offer detailed images of the female pelvis, with no risk related to radiation dose exposure. Particularly, MRI is considered the non-invasive standard of reference technique in the gynaecological field, both for diagnosis and management [1]. Classical CT and MRI semiotics have, however, some limitations in the precise characterization and prediction of prognosis of some gynaecological malignancies.

2. Application of Radiomics in the Female Pelvis: From Segmentation to Features Extraction

To obtain a quantitative reliable result in radiomics studies, it is fundamental to follow a precise radiomics pipeline, composed of five necessary steps: (1) image acquisition, (2) segmentation, (3) features extraction, (4) features selection, and (5) statistical analysis and modelling [4]. All of them should be provided in the most robust way possible, especially to allow reproducibility between studies and centres.
Image acquisition is the first and one of the most important steps. When setting up a radiomics study, imaging protocol(s) must be well delineated (e.g., contrast media administration, timing of dynamic sequences, mandatory and optional MRI sequences). Data regarding CT and MRI protocols for the study of the female pelvis have been comprehensively detailed in the literature [5][6].
Segmentation is a fundamental step in medical image analysis as it allows for the identification and isolation of specific anatomic structures or pathological regions within an image. For computing this aspect, region(s) of interest (ROIs) or volume(s) of interest (VOIs) should be drawn in the interested lesion, organ, or tissue. ROIs can be manually drawn by human readers, semi-automatically or automatically, each of them with advantages and disadvantages.
Manual segmentation has the main disadvantage of being time consuming: the reader should sketch the contours slice by slice using pointing devices. Moreover, this procedure can lead to a reduction in the robustness of radiomics features [7].
On the other hand, semi-automatic and automatic approaches are based on available software or custom-made algorithms; in the case of the semi-automatic approach, the preliminary segmentation will be refined by a human reader [7][8]. Semi-automatic segmentation strategies, providing putative contours that the expert operator is asked to refine or correct, represent a solution to reduce both times and inter-operator variability. Automatic segmentation techniques rely on deep or machine learning (DL and ML, respectively) strategies [9]. These models learn step by step how to segment images if trained on a large amount of already labelled images.
Once the ROI or VOI is segmented, a wide range of quantitative features can be extracted. These features can be categorized into different groups such as shape-based features, intensity-based features, texture-based features, and spatial-relationship-based features. The process that leads to obtaining this quantitative data is named “feature extraction”. After that, feature selection is employed to identify the most relevant and discriminative features for a specific clinical task. This step helps reduce the computational burden and improve the robustness of subsequent analysis.
Finally, the extracted and selected radiomic features are subjected to statistical analysis and modelling. Various statistical methods, machine learning algorithms, or deep learning architectures can be applied to explore the relationships between these features and clinical outcomes or other relevant parameters [10].
One of the most important limitations to be underlined regards radiomics features obtained by different scanners and institutions. In fact, radiomic features are usually strictly dependent on different factors, such as acquisition data, MR technical features, and contrast media, being employed. For these reasons, whether it is necessary to compare radiomics data deriving from different scanners or from multiple institutions, it is of utmost importance to provide a post-processing step, in order to reduce potential bias. Different methods were proposed, including denoising [11], N4 bias field correction [12], voxel size resampling and interpolation, discretization [13], and ComBat harmonization [14].

3. Endometrial Cancer

Endometrial cancer (EC) is the most common gynaecological malignancy in industrialized countries, with an expected increasing incidence worldwide [15][16][17]. EC usually affects postmenopausal patients (75–80% of cases), with a peak between 55 and 65 years, and its most frequent clinical manifestation is abnormal postmenopausal bleeding [15][18].
Diagnosis is based on minimally invasive procedures (hysteroscopy, endometrial biopsy, dilatation, and curettage) and transvaginal ultrasound (TVUS) [18][19]. However, MRI is considered the best technique for pre-operative staging [20][21].
EC is classified according to the recently updated International Federation of Gynaecology and Obstetrics (FIGO) staging system [22] and is traditionally grouped into two major prognostic groups (type I and type II), based on the histological type and FIGO histological grading system [23][24][25]. Type I tumours account for approximately 80% of endometrial neoplasms and include endometrioid histotypes with pathological grading G1 and G2. This category of EC is characterized by a good prognosis and is typically estrogen responsive. Type II represents approximately 20% of overall endometrial tumours and includes high-grade (G3) endometrioid and non-endometrioid forms (clear-cell, mucinous, carcinosarcomas, undifferentiated forms). This group usually shows a more aggressive course and does not correlate with estrogenic exposure.
In order to provide therapeutic management guidelines, the European Society of Gynaecological Oncology (ESMO), European Society for Radiotherapy and Oncology (ESTRO), and European Society of Gynaecological Oncology (ESGO) guidelines stratify EC into four risk categories (low, intermediate, high intermediate, and high risk) according to histology, grade, stage, and the presence of lymph vascular space invasion (LVSI), which describe tumour behaviour and the likelihood of recurrences, directing toward possible adjuvant therapy [18]. EC clinical behaviour is also influenced by its genomic features; therefore, a reclassification of EC based on genomics has been recently proposed considering four categories (POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high) [26].
Deep myometrial invasion (DMI) allows for the differentiation between the FIGO stages IA and IB, the most important morphological prognostic factor [27][28]. Even if MRI is the most accurate technique for the evaluation of DMI, its detection is not always straightforward and has shown relatively high inter-observer variability, particularly when the endometrium is thinned (i.e., older patients, endometrial cavity distension), when the endometrial–myometrial interface is obscured by fibroids or adenomyosis, and when the lesion is located in uterine cornual regions [27][28][29][30].
Another key prognostic factor is the presence of pelvic or para-aortic nodes metastases (LNM), which is also essential in guiding the therapeutic approach. The principal criterion for suspecting nodes metastases is based on nodal size (short axis > 1 cm). However, this criterion has a low specificity because hyperplastic nodes can also be enlarged, which may lead to a high percentage of false-positive results. At the same time, it has been demonstrated that nodes with a short axis lower than 1 cm are sometimes proved as metastatic at pathology [20][31].
Many studies investigated the possible role of radiomics in improving the assessment of EC [32][33]. The most common imaging modality employed in radiomics investigation studies for EC diagnosis and staging was MRI, using standard diagnostic MRI protocols. According to the latest guidelines of the European Society of Urogenital Radiology (ESUR), pelvic MRI protocol for EC assessment includes T2WI, diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging [20].

4. Cervical Cancer

Cervical cancer (CC) is ranked fourth among malignancies in terms of incidence, prevalence, and mortality in women worldwide; however, when early diagnosis is made, CC is one of the most successfully treatable forms of cancer [34]. Patients with CC are staged according to the TNM classification, and the clinical staging is based on the FIGO staging system [35]. The initial workup for assessment of pelvic tumour extent and to guide treatment options is based on pelvic MRI according to consensus recommendations by the ESMO, ESTRO, and European Society of Pathology (ESP) guidelines [36].
Radiological assessment of cervical malignancy for the initial staging, response monitoring, and evaluation of disease recurrence is performed on pelvic MRI according to the ESUR guidelines [37]. The standard pelvic MRI protocol includes T1WI without and with saturation, T2WI with a slice thickness of 4 mm or less, and DWI sequences while contrast-enhanced MRI remains optional. However, pelvic MRI has some limitations in the assessment of CC, mainly related to the limited accuracy for nodes status, largely based on the size criteria (i.e., ≥1.0 cm in short axis) which yields a low pooled sensitivity of 56–61% [38], in the assessment of parametrial invasion with a pooled sensitivity of 76% and specificity of 94% [39], as well as in the evaluation of residual disease after chemoradiation therapy with sensitivity and specificity of 80% and 55%, respectively [40]. Given the current limitations of standard imaging techniques, over the last decade, there has been an increasing number of studies investigating if radiomics applied to CT or MRI may fill the current gaps in patients with CC.
Most of these radiomics studies proved a moderate to high performance of radiomics or combined clinical–radiomics models suggesting that radiomics might be used as a prognostic biomarker and helpful in tailoring therapeutic management. In addition, some papers addressed technical issues about radiomics, including the development of a fully automatic whole-volume tumour segmentation tool [41], evaluation of robustness, stability, and reproducibility of radiomic features in pelvic MRI, suggesting the application of normalization prior to features extraction [42][43][44][45] and the definition of the best volume of interest to achieve a specific outcome [46].

5. Mesenchymal Tumours

Uterine mesenchymal tumours arise from uterine smooth muscle, endometrial stroma, or a combination of both [47]. Benign leiomyomas (LM) are the most common mesenchymal uterine tumours, affecting up to 80% of women of reproductive age [48]. Conversely, uterine sarcomas (US) are a rare form of mesenchymal tumours, accounting for approximately 1% of gynaecological neoplasms and 3–7% of all uterine malignancies, and have a poor prognosis [49]. Currently, there are no reliable imaging criteria for distinguishing US, especially leiomyosarcomas, from LM with atypical features, including degeneration or unusual pattern of growth [50]. The final diagnosis is usually made only after the surgery, based on postoperative histopathological assessment.
Considering the overlap imaging features in atypical LM and US, several authors investigated the possible role of MRI texture analysis in aiding radiologists in the differential diagnosis between these two entities [51]. However, the majority of these publications are retrospective and monocentric, beyond being limited by small sample cohorts of patients, particularly those with malignant lesions.
Malek et al. developed a radiomic model based on MR perfusion, which showed good diagnostic values (accuracy, sensitivity, and specificity of 91%, 100%, and 90%, respectively) [52]. The same authors aimed to develop a decision tree and a complex algorithm to differentiate US and LM, with accuracies of 96% and 100%, respectively. However, the algorithm was reported to be time consuming, with a special limit for everyday clinical practice [53].
Finally, Xie et al. reported that a radiomic model based on ADC map can predict pathological results of patients with sarcomas and atypical leiomyomas with an AUC of 0.83 [54].
Nakagawa and colleagues compared the performance of ML using multiparametric MRI and positron-emission tomography (PET), concluding that the MRI-based model was superior to the PET one and comparable with that of experienced radiologists [55]. Another investigation compared the diagnostic performance of three different volumes of interests (VOIs)—lesion, lesion and surrounding tissue, and whole uterus—in ADC map-based radiomic analysis for distinguishing US and LM. The results showed that the model based on features extracted from VOIs covering the whole uterus had the best diagnostic performance (AUC: 0.876, sensitivity: 76.3%, and specificity: 84.5%) [56]. Yang and Stamp focused their research on distinguishing low-grade US and LM, testing different ML models and various cutting-edge deep learning techniques. For the classic techniques considered, the highest classification accuracy was 0.85, while the most accurate learning model achieved an accuracy of approximately 0.87 [57].

6. Ovarian Pathologies

Ovarian lesions are a frequent cause of gynaecological pathologies with both benign and malignant conditions frequently encountered in clinical practice. Ovarian cancer is in the seventh place for cancer incidence in females and it is associated with high mortality [58]. Epithelial tumours are the most common cause of ovarian cancer, and they include a wide spectrum of lesions with different histopathological features, risk factors, treatment options, and prognosis [58]. Particularly, the most common ovarian lesions are serous and mucinous tumours. In this complex clinical context, radiomics can serve as a relevant tool to improve the diagnosis, management, and prediction of prognosis in patients with ovarian pathologies [59]. Several studies explored the performance of radiomics with a plethora of different aims and outcomes in ovarian pathologies. 
Ultrasound, CT, and MRI are the most used imaging techniques in patients with ovarian pathologies. Ultrasound is the first imaging modality for the assessment of ovarian pathologies but the differential diagnosis between different entities may be challenging based only on the qualitative assessment. Initial single-centre studies applied the radiomics analysis on ultrasound images to predict the histopathological types and grades of epithelial ovarian cancers [60][61] and prognosis [62] with reported good performances but validation can be problematic as the ultrasound images’ acquisitions depend on operator experience.

This entry is adapted from the peer-reviewed paper 10.3390/app132111839

References

  1. Gjelsteen, A.C.; Ching, B.H.; Meyermann, M.W.; Prager, D.A.; Murphy, T.F.; Berkey, B.D.; Mitchell, L.A. CT, MRI, PET, PET/CT, and ultrasound in the evaluation of obstetric and gynecologic patients. Surg. Clin. N. Am. 2008, 88, 361–390.
  2. Forstner, R.; Cunha, T.M.; Hamm, B. (Eds.) MRI and CT of the Female Pelvis, 2nd ed.; Medical Radiology: Diagnostic Imaging; Springer: Cham, Switzerland, 2019; ISBN 9783319425733.
  3. Daoud, T.; Sardana, S.; Stanietzky, N.; Klekers, A.R.; Bhosale, P.; Morani, A.C. Recent Imaging Updates and Advances in Gynecologic Malignancies. Cancers 2022, 14, 5528.
  4. Shur, J.D.; Doran, S.J.; Kumar, S.; Ap Dafydd, D.; Downey, K.; O’Connor, J.P.B.; Papanikolaou, N.; Messiou, C.; Koh, D.-M.; Orton, M.R. Radiomics in Oncology: A Practical Guide. RadioGraphics 2021, 41, 1717–1732.
  5. Nougaret, S.; Lakhman, Y.; Gourgou, S.; Kubik-Huch, R.; Derchi, L.; Sala, E.; Forstner, R.; The European Society of Radiology (ESR) and the European Society of Urogenital Radiology (ESUR). MRI in Female Pelvis: An ESUR/ESR Survey. Insights Imaging 2022, 13, 60.
  6. O’Malley, M.E.; Halpern, E.; Mueller, P.R.; Gazelle, G.S. Helical CT Protocols for the Abdomen and Pelvis: A Survey. Am. J. Roentgenol. 2000, 175, 109–113.
  7. Zhou, S.K.; Rueckert, D.; Fichtinger, G. (Eds.) Handbook of Medical Image Computing and Computer Assisted Intervention; The Elsevier and MICCAI Society Book Series; Academic Press: London, UK; San Diego, CA, USA, 2020; ISBN 9780128161760.
  8. Ma, Z.; Tavares, J.M.R.S.; Jorge, R.N.; Mascarenhas, T. A Review of Algorithms for Medical Image Segmentation and Their Applications to the Female Pelvic Cavity. Comput. Methods Biomech. Biomed. Eng. 2010, 13, 235–246.
  9. Navab, N.; Hornegger, J.; Wells, W.M.; Frangi, A. (Eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015; Springer: New York, NY, USA, 2015; ISBN 9783319245737.
  10. Avanzo, M.; Wei, L.; Stancanello, J.; Vallières, M.; Rao, A.; Morin, O.; Mattonen, S.A.; El Naqa, I. Machine and Deep Learning Methods for Radiomics. Med. Phys. 2020, 47, e185–e202.
  11. Lee, J.; Jeon, J.; Hong, Y.; Jeong, D.; Jang, Y.; Jeon, B.; Baek, H.J.; Cho, E.; Shim, H.; Chang, H.-J. Generative Adversarial Network with Radiomic Feature Reproducibility Analysis for Computed Tomography Denoising. Comput. Biol. Med. 2023, 159, 106931.
  12. Foltyn-Dumitru, M.; Schell, M.; Rastogi, A.; Sahm, F.; Kessler, T.; Wick, W.; Bendszus, M.; Brugnara, G.; Vollmuth, P. Impact of Signal Intensity Normalization of MRI on the Generalizability of Radiomic-Based Prediction of Molecular Glioma Subtypes. Eur. Radiol. 2023, 1–9.
  13. Kocak, B.; Yuzkan, S.; Mutlu, S.; Bulut, E.; Kavukoglu, I. Publications Poorly Report the Essential RadiOmics ParametERs (PROPER): A Meta-Research on Quality of Reporting. Eur. J. Radiol. 2023, 167, 111088.
  14. Orlhac, F.; Frouin, F.; Nioche, C.; Ayache, N.; Buvat, I. Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics. Radiology 2019, 291, 53–59.
  15. Crosbie, E.J.; Kitson, S.J.; McAlpine, J.N.; Mukhopadhyay, A.; Powell, M.E.; Singh, N. Endometrial Cancer. Lancet 2022, 399, 1412–1428.
  16. Moore, K.; Brewer, M.A. Endometrial Cancer: Is This a New Disease? Am. Soc. Clin. Oncol. Educ. Book 2017, 37, 435–442.
  17. Sorosky, J.I. Endometrial Cancer. Obstet. Gynecol. 2012, 120, 383–397.
  18. Colombo, N.; Creutzberg, C.; Amant, F.; Bosse, T.; González-Martín, A.; Ledermann, J.; Marth, C.; Nout, R.; Querleu, D.; Mirza, M.R.; et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: Diagnosis, Treatment and Follow-Up. Ann. Oncol. 2016, 27, 16–41.
  19. Braun, M.M.; Overbeek-Wager, E.A.; Grumbo, R.J. Diagnosis and Management of Endometrial Cancer. Am. Fam. Physician 2016, 93, 468–474.
  20. Nougaret, S.; Horta, M.; Sala, E.; Lakhman, Y.; Thomassin-Naggara, I.; Kido, A.; Masselli, G.; Bharwani, N.; Sadowski, E.; Ertmer, A.; et al. Endometrial Cancer MRI Staging: Updated Guidelines of the European Society of Urogenital Radiology. Eur. Radiol. 2019, 29, 792–805.
  21. Faria, S.C.; Devine, C.E.; Rao, B.; Sagebiel, T.; Bhosale, P. Imaging and Staging of Endometrial Cancer. Semin. Ultrasound CT MR 2019, 40, 287–294.
  22. Berek, J.S.; Matias-Guiu, X.; Creutzberg, C.; Fotopoulou, C.; Gaffney, D.; Kehoe, S.; Lindemann, K.; Mutch, D.; Concin, N.; Endometrial Cancer Staging Subcommittee; et al. FIGO Staging of Endometrial Cancer: 2023. Int. J. Gynecol. Obstet. 2023, 162, 383–394.
  23. Ulrich, L.S.G. Endometrial Cancer, Types, Prognosis, Female Hormones and Antihormones. Climacteric 2011, 14, 418–425.
  24. Murali, R.; Soslow, R.A.; Weigelt, B. Classification of Endometrial Carcinoma: More than Two Types. Lancet Oncol. 2014, 15, e268–e278.
  25. Bokhman, J.V. Two Pathogenetic Types of Endometrial Carcinoma. Gynecol. Oncol. 1983, 15, 10–17.
  26. Cancer Genome Atlas Research Network; Kandoth, C.; Schultz, N.; Cherniack, A.D.; Akbani, R.; Liu, Y.; Shen, H.; Robertson, A.G.; Pashtan, I.; Shen, R.; et al. Integrated Genomic Characterization of Endometrial Carcinoma. Nature 2013, 497, 67–73.
  27. Larson, D.M.; Connor, G.P.; Broste, S.K.; Krawisz, B.R.; Johnson, K.K. Prognostic Significance of Gross Myometrial Invasion with Endometrial Cancer. Obstet. Gynecol. 1996, 88, 394–398.
  28. Ludwig, H. Prognostic Factors in Endometrial Cancer. Int. J. Gynaecol. Obstet. 1995, 49, S1–S7.
  29. Concin, N.; Matias-Guiu, X.; Vergote, I.; Cibula, D.; Mirza, M.R.; Marnitz, S.; Ledermann, J.; Bosse, T.; Chargari, C.; Fagotti, A.; et al. ESGO/ESTRO/ESP Guidelines for the Management of Patients with Endometrial Carcinoma. Int. J. Gynecol. Cancer 2021, 31, 12–39.
  30. Otero-García, M.M.; Mesa-Álvarez, A.; Nikolic, O.; Blanco-Lobato, P.; Basta-Nikolic, M.; de Llano-Ortega, R.M.; Paredes-Velázquez, L.; Nikolic, N.; Szewczyk-Bieda, M. Role of MRI in Staging and Follow-up of Endometrial and CC: Pitfalls and Mimickers. Insights Imaging 2019, 10, 19.
  31. Bús, D.; Nagy, G.; Póka, R.; Vajda, G. Clinical Impact of Preoperative Magnetic Resonance Imaging in the Evaluation of Myometrial Infiltration and Lymph-Node Metastases in Stage I Endometrial Cancer. Pathol. Oncol. Res. 2021, 27, 611088.
  32. Manganaro, L.; Nicolino, G.M.; Dolciami, M.; Martorana, F.; Stathis, A.; Colombo, I.; Rizzo, S. Radiomics in Cervical and Endometrial Cancer. Br. J. Radiol. 2021, 94, 20201314.
  33. Wang, Y.; Chen, Z.; Liu, C.; Chu, R.; Li, X.; Li, M.; Yu, D.; Qiao, X.; Kong, B.; Song, K. Radiomics-Based Fertility-Sparing Treatment in Endometrial Carcinoma: A Review. Insights Imaging 2023, 14, 127.
  34. CC. Available online: https://www.who.int/health-topics/cervical-cancer (accessed on 17 September 2023).
  35. Bhatla, N.; Berek, J.S.; Cuello Fredes, M.; Denny, L.A.; Grenman, S.; Karunaratne, K.; Kehoe, S.T.; Konishi, I.; Olawaiye, A.B.; Prat, J.; et al. Revised FIGO Staging for Carcinoma of the Cervix Uteri. Int. J. Gynaecol. Obstet. 2019, 145, 129–135.
  36. Cibula, D.; Pötter, R.; Planchamp, F.; Avall-Lundqvist, E.; Fischerova, D.; Haie-Meder, C.; Köhler, C.; Landoni, F.; Lax, S.; Lindegaard, J.C.; et al. The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology Guidelines for the Management of Patients with CC. Virchows Arch. 2018, 472, 919–936.
  37. Manganaro, L.; Lakhman, Y.; Bharwani, N.; Gui, B.; Gigli, S.; Vinci, V.; Rizzo, S.; Kido, A.; Cunha, T.M.; Sala, E.; et al. Staging, Recurrence and Follow-up of Uterine CC Using MRI: Updated Guidelines of the European Society of Urogenital Radiology after Revised FIGO Staging 2018. Eur. Radiol. 2021, 31, 7802–7816.
  38. Liu, B.; Gao, S.; Li, S. A Comprehensive Comparison of CT, MRI, Positron Emission Tomography or Positron Emission Tomography/CT, and Diffusion Weighted Imaging-MRI for Detecting the Lymph Nodes Metastases in Patients with CC: A Meta-Analysis Based on 67 Studies. Gynecol. Obstet. Investig. 2017, 82, 209–222.
  39. Woo, S.; Suh, C.H.; Kim, S.Y.; Cho, J.Y.; Kim, S.H. Magnetic Resonance Imaging for Detection of Parametrial Invasion in CC: An Updated Systematic Review and Meta-Analysis of the Literature between 2012 and 2016. Eur. Radiol. 2018, 28, 530–541.
  40. Vincens, E.; Balleyguier, C.; Rey, A.; Uzan, C.; Zareski, E.; Gouy, S.; Pautier, P.; Duvillard, P.; Haie-Meder, C.; Morice, P. Accuracy of Magnetic Resonance Imaging in Predicting Residual Disease in Patients Treated for Stage IB2/II Cervical Carcinoma with Chemoradiation Therapy: Correlation of Radiologic Findings with Surgicopathologic Results. Cancer 2008, 113, 2158–2165.
  41. Hodneland, E.; Kaliyugarasan, S.; Wagner-Larsen, K.S.; Lura, N.; Andersen, E.; Bartsch, H.; Smit, N.; Halle, M.K.; Krakstad, C.; Lundervold, A.S.; et al. Fully Automatic Whole-Volume Tumour Segmentation in CC. Cancers 2022, 14, 2372.
  42. Fiset, S.; Welch, M.L.; Weiss, J.; Pintilie, M.; Conway, J.L.; Milosevic, M.; Fyles, A.; Traverso, A.; Jaffray, D.; Metser, U.; et al. Repeatability and Reproducibility of MRI-Based Radiomic Features in CC. Radiother. Oncol. 2019, 135, 107–114.
  43. Traverso, A.; Kazmierski, M.; Welch, M.L.; Weiss, J.; Fiset, S.; Foltz, W.D.; Gladwish, A.; Dekker, A.; Jaffray, D.; Wee, L.; et al. Sensitivity of Radiomic Features to Inter-Observer Variability and Image Pre-Processing in Apparent Diffusion Coefficient (ADC) Maps of Cervix Cancer Patients. Radiother. Oncol. 2020, 143, 88–94.
  44. Ramli, Z.; Karim, M.K.A.; Effendy, N.; Abd Rahman, M.A.; Kechik, M.M.A.; Ibahim, M.J.; Haniff, N.S.M. Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on CC DWI-MRI. Diagnostics 2022, 12, 3125.
  45. Chen, H.; He, Y.; Zhao, C.; Zheng, L.; Pan, N.; Qiu, J.; Zhang, Z.; Niu, X.; Yuan, Z. Reproducibility of Radiomics Features Derived from Intravoxel Incoherent Motion Diffusion-Weighted MRI of CC. Acta Radiol. 2021, 62, 679–686.
  46. Takada, A.; Yokota, H.; Watanabe Nemoto, M.; Horikoshi, T.; Matsushima, J.; Uno, T. A Multi-Scanner Study of MRI Radiomics in Uterine CC: Prediction of in-Field Tumour Control after Definitive Radiotherapy Based on a Machine Learning Method Including Peritumoural Regions. Jpn. J. Radiol. 2020, 38, 265–273.
  47. Capozzi, V.A.; Monfardini, L.; Ceni, V.; Cianciolo, A.; Butera, D.; Gaiano, M.; Berretta, R. Endometrial Stromal Sarcoma: A Review of Rare Mesenchymal Uterine Neoplasm. J. Obstet. Gynaecol. Res. 2020, 46, 2221–2236.
  48. Baird, D.D.; Dunson, D.B.; Hill, M.C.; Cousins, D.; Schectman, J.M. High Cumulative Incidence of Uterine Leiomyoma in Black and White Women: Ultrasound Evidence. Am. J. Obstet. Gynecol. 2003, 188, 100–107.
  49. D’Angelo, E.; Prat, J. Uterine Sarcomas: A Review. Gynecol. Oncol. 2010, 116, 131–139.
  50. Wang, C.; Zheng, X.; Zhou, Z.; Shi, Y.; Wu, Q.; Lin, K. Differentiating Cellular Leiomyoma from Uterine Sarcoma and Atypical Leiomyoma Using Multi-Parametric MRI. Front. Oncol. 2022, 12, 1005191.
  51. Causa Andrieu, P.; Woo, S.; Kim, T.-H.; Kertowidjojo, E.; Hodgson, A.; Sun, S. New Imaging Modalities to Distinguish Rare Uterine Mesenchymal Cancers from Benign Uterine Lesions. Curr. Opin. Oncol. 2021, 33, 464–475.
  52. Malek, M.; Gity, M.; Alidoosti, A.; Oghabian, Z.; Rahimifar, P.; Seyed Ebrahimi, S.M.; Tabibian, E.; Oghabian, M.A. A Machine Learning Approach for Distinguishing Uterine Sarcoma from Leiomyomas Based on Perfusion Weighted MRI Parameters. Eur. J. Radiol. 2019, 110, 203–211.
  53. Malek, M.; Tabibian, E.; Rahimi Dehgolan, M.; Rahmani, M.; Akhavan, S.; Sheikh Hasani, S.; Nili, F.; Hashemi, H. A Diagnostic Algorithm Using Multi-Parametric MRI to Differentiate Benign from Malignant Myometrial Tumours: Machine-Learning Method. Sci. Rep. 2020, 10, 7404.
  54. Xie, H.; Hu, J.; Zhang, X.; Ma, S.; Liu, Y.; Wang, X. Preliminary Utilization of Radiomics in Differentiating Uterine Sarcoma from Atypical Leiomyoma: Comparison on Diagnostic Efficacy of MRI Features and Radiomic Features. Eur. J. Radiol. 2019, 115, 39–45.
  55. Nakagawa, M.; Nakaura, T.; Namimoto, T.; Iyama, Y.; Kidoh, M.; Hirata, K.; Nagayama, Y.; Oda, S.; Sakamoto, F.; Shiraishi, S.; et al. A Multiparametric MRI-Based Machine Learning to Distinguish between Uterine Sarcoma and Benign Leiomyoma: Comparison with 18F-FDG PET/CT. Clin. Radiol. 2019, 74, 167.e1–167.e7.
  56. Xie, H.; Zhang, X.; Ma, S.; Liu, Y.; Wang, X. Preoperative Differentiation of Uterine Sarcoma from Leiomyoma: Comparison of Three Models Based on Different Segmentation Volumes Using Radiomics. Mol. Imaging Biol. 2019, 21, 1157–1164.
  57. Yang, X.; Stamp, M. Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS). Comput. Biol. Med. 2021, 138, 104874.
  58. Nougaret, S.; Tardieu, M.; Vargas, H.A.; Reinhold, C.; Vande Perre, S.; Bonanno, N.; Sala, E.; Thomassin-Naggara, I. Ovarian Cancer: An Update on Imaging in the Era of Radiomics. Diagn. Interv. Imaging 2019, 100, 647–655.
  59. Vernuccio, F.; Cannella, R.; Comelli, A.; Salvaggio, G.; Lagalla, R.; Midiri, M. Radiomica e intelligenza artificiale: Nuove frontiere in medicina. Recent. Progress. Med. 2020, 111, 130–135.
  60. Qi, L.; Chen, D.; Li, C.; Li, J.; Wang, J.; Zhang, C.; Li, X.; Qiao, G.; Wu, H.; Zhang, X.; et al. Diagnosis of Ovarian Neoplasms Using Nomogram in Combination with Ultrasound Image-Based Radiomics Signature and Clinical Factors. Front. Genet. 2021, 12, 753948.
  61. Yao, F.; Ding, J.; Lin, F.; Xu, X.; Jiang, Q.; Zhang, L.; Fu, Y.; Yang, Y.; Lan, L. Nomogram Based on Ultrasound Radiomics Score and Clinical Variables for Predicting Histologic Subtypes of Epithelial Ovarian Cancer. Br. J. Radiol. 2022, 95, 20211332.
  62. Yao, F.; Ding, J.; Hu, Z.; Cai, M.; Liu, J.; Huang, X.; Zheng, R.; Lin, F.; Lan, L. Ultrasound-Based Radiomics Score: A Potential Biomarker for the Prediction of Progression-Free Survival in Ovarian Epithelial Cancer. Abdom. Radiol. 2021, 46, 4936–4945.
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