Computed Tomography Features Show Excellent Inter-radiologist Reproducibility and Malignancy Grading for Ovarian Teratomas.

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Saeed Mohammadzadeh, Alisa Mohebbi, Ali Abdi, Ali Abbasian Ardakani, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8262082/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: To evaluate the reproducibilities of computed tomography (CT) features among patients with ovarian teratoma tumors. Methods: This retrospective study included 32 patients with confirmed mature cystic teratoma and embryonic teratoma, recruited between January 2008 and June 2021. The study protocol has been pre-registered at (https://osf.io/n6fw5/) on the Open Science Framework (OSF) platform. Patients under the age of 20 who received transabdominal ultrasonography and/or abdominal and pelvic CT scans prior to surgery and underwent surgery at our institution were included. A gynecologic oncologist and a board-certified radiologist, both having extensive experience, interpreted CT scans for the following specifications: a) maximum diameter of calcifications (cm), b) number of calcifications, c) maximum diameter of fats (cm), and d) number of fats. Results: The intraclass correlation coefficient (ICC) for maximum diameter of calcifications, number of calcifications, maximum fat diameter, and number of fat were 0.994 (CI = 0.980 to 0.998), 0.991 (CI = 0.975 to 0.997), 0.988 (CI = 0.967 to 0.996), and 0.977 (CI = 0.936 to 0.993), respectively. Also, the maturity of the tumor was more strongly correlated to the number of fat, the maximum diameter of calcifications, and the number of calcifications, with Pearson correlation coefficients of 0.846, 0.806, and 0.682, respectively. Conclusion: Our findings demonstrated excellent inter-reader agreement for all four CT measurements. This indicates a high consistency in CT-based evaluations of ovarian teratomas, which could enhance diagnostic accuracy and inform clinical decision-making. Nuclear Medicine & Medical Imaging Ovarian Teratoma Computed Tomography (CT) Calcification Fat Diagnostic Test Accuracy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mature teratoma, often referred to as mature cystic teratoma (MCT), consists solely of fully developed tissues originating from two or three germ layers, specifically ectoderm, mesoderm, and endoderm. It is the most common ovarian tumor in children and adolescents, accounting for over 50% of occurrences in females under the age of 20 ( 1 ). However, it can be observed in women of all ages ( 2 ). The patients may exhibit no symptoms, or they may experience chronic pelvic pain or the presence of a pelvic mass. The tumors are typically discovered coincidentally during imaging taken for other purposes, as almost 20% of patients do not exhibit any symptoms after MCT is detected ( 3 , 4 ). The most typical ultrasound (US) image of an MCT is an echogenic nodule containing echogenic sebaceous material and calcifications ( 5 , 6 ). The US has a sensitivity ranging from 58% to 92.7% and a specificity ranging from 87.5% to 99% for the diagnosis of adult teratomas. However, US may lead to misdiagnosis of teratomas in the presence of other pelvic malignancies, including fibromas and endometriosis ( 7 ). Tumors that cannot be definitively identified in the US can be further assessed using computed tomography (CT). CT has a higher diagnostic performance than the US in detecting MCTs due to its superior capability in detecting intratumoral fat and calcifications, resulting in a sensitivity of 93% to 98% ( 6 ). The occurrence of teeth within the Rokitansky nodule is characteristic of fully developed teratoma. Also, curvilinear calcifications might be observed within the tumor's septa or wall ( 8 ). The quantity of fat present in immature teratoma varies. Specifically, certain tumors may appear on imaging with very little or no discernible fat. The cystic fluid in immature teratomas has a lower fat content compared to the sebaceous fluid in MCTs ( 9 , 10 ). Therefore, the CT attenuation of the fluid within young teratoma is frequently more than that of its mature equivalent, resembling the attenuation of simple fluid (0–10 HU) ( 11 ). Throughout clinical practice, images are interpreted by radiologists who possess different levels of expertise. Assessing the collective opinion of readers is crucial, particularly when it influences the differential diagnosis and, subsequently, patient medical treatment. The objective of our study was to examine the concordance among various readers when utilizing CT images to evaluate the tumors of ovarian teratoma in order to optimize reporting systems and guide clinical decision-making. Material and Methods The Handbook of Inter-Rater Reliability ( 12 , 13 ) has been used as the basis for the methodology and analysis in this investigation. Also, we adhered to the principles outlined in the Declaration of Helsinki. The Institutional Review Board of the University of Fukui Hospital (approval number: 20210730) approved the study. Patients supplied written informed consent to participate in the study and were offered an opt-out option. Anonymized clinical data were utilized. Participants were retrospectively enrolled from a dataset titled as “Evaluation of calcification distribution by CT-based textural analysis for discrimination of immature teratoma” from Figshare ( 14 ). The study protocol has been registered a priori at ( https://osf.io/n6fw5/ ) on the Open Science Framework (OSF) platform (Appendix A). Patient selection: We retrospectively examined the medical records of 32 patients from Fukui Hospital who were recruited between January 2008 and June 2021 and had histopathologically confirmed MCT (n = 28) and embryonic teratomas (n = 4). Ten patients with abdominal CT data were referred from other institutions, making this study a multicentral investigation. The study included patients who were under the age of 20, received transabdominal ultrasonography and/or abdominal and pelvic CT scans prior to surgery, and underwent surgery at our institution. In addition, tumor characteristics such as maximum diameter (cm), mean diameter (cm), volume (cm3) as well as blood level of alpha-fetoprotein (AFP), CA125, CA19-9 were collected. Image interpretation: A 64-slice multidetector CT scanner (Discovery CT750HD; GE Medical Systems, Milwaukee, WI, USA) was used to conduct abdominal and pelvic non-contrast CT exams. The analysis of images was performed using the picture archiving and communication system, and Ziostation2 software (Ziosoft, Tokyo, Japan) was utilized to create a three-dimensional image. The CT images were retrospectively interpreted by a gynecologic oncologist and a board-certified radiologist, both having extensive experience of 21 and 24 years, respectively. Both specialists have double certifications and specialize in gynecological imaging. The images were assessed for the following specifications: a) maximum diameter of calcifications (cm), b) number of calcifications, c) maximum diameter of fats (cm), and d) number of fats. The cut point for identifying calcified lesions was established as a CT density of 130 Hounsfield units, while fat was defined by a CT density ranging from − 144 to -20 Hounsfield units ( 15 , 16 ). Both readers were blinded to the outcomes of the other imaging examinations, clinical data, and histological findings. Texture analysis: The CT images of the patients were extracted from the picture archiving and communication system in DICOM format and sent to the LIFEx software version 7.2.0 (LITO, CEA, Inserm, CNRS, Univ. Paris-Sud, Université Paris Saclay) ( 17 ). The procedure was carried out independently by a gynecologic oncologist and a board-certified radiologist who were blinded to the tumor diagnosis. This was done to minimize any bias when assessing the radiomic features. They meticulously outlined each target lesion along its contour and extracted calcifications and fats manually. Prior to calculating radiomic features, the voxel intensities were resampled using the relative resampling approach, employing a fixed bin number of 128. Statistical analysis: The statistical analyses were performed using Stata 17.0 and Medcalc 22.0. To assess the agreement of quantitative variables, we computed the intraclass correlation coefficient (ICC) type 1. The findings of type 1 ICC can be generalized from our specific patient sample to the broader patient population. We defined agreement classification as poor when ICC was < 0.50, moderate when 0.50–0.75, good when 0.75–0.90, and excellent when ≥ 0.90 ( 18 ). In addition, Bland-Altman (BA) graphs were generated to evaluate the visual interpretation of the agreement further. A p-value < 0.05 was determined to have statistical significance. Results Patient characteristics: A total of 32 female patients were involved in our database. The mean age of patients was 14.5 (ranging from 6 to 19). Of these patients, 28 (87.5%) presented with MCT. The remaining 4 patients (12.5%) had immature teratomas. These immature teratomas included 2 cases of grade 1, 1 case of grade 2, and 1 case of grade 3. In addition, 26 patients (81.2%) experienced symptoms such as stomach pain and distension, whereas the remaining 6 patients (18.8%) were asymptomatic. A summary of the imaging features and clinical data of the patients in regard to tumor malignancy status is provided in Table 1 . Table 1 characteristics of included patients. immature mature Number of patients 4 28 BMI (kg/m2) Means (range) 19.8 (16.9–21.2) 20.1 (15.3–25.2) Age (year) Means (range) 14.0 ( 11 – 18 ) 14.5 ( 6 – 19 ) Maximum diameter (cm) Means (range) 21.5 (18.0–25.0) 10.3 (3.0–27.0) Mean diameter (cm) Means (range) 15.8 (14.3–17.7) 8.7 (2.7–18.3) Volume (cm3) Means (range) 3451 (2660–4320) 969.7 (18.8–5049) Symptoms 4 (100%) 22 (78.6%) AFP (ng/mL) Means (range) 101.7 (3.8–185.2) 2.1 (0.6–5.7) CA19-9 (U/mL) Means (range) 406.3 (24.8–1438) 135.6 (3.6–1350) CA125 (U/mL) Means (range) 289.4 (30.6–885.0) 36.2 (8.8–173.9) CT scan inter-reader agreement: We assessed the level of agreement between two readers in measuring calcifications and fat content within the structures using ICC. Only 14 patients with non-contrast CT were included in this analysis. The ICC for the maximum diameter of calcifications and the number of calcifications were 0.994 (CI = 0.980 to 0.998) and 0.991 (CI = 0.975 to 0.997), respectively. Similarly, high agreement was observed for fat measurements, with an ICC of 0.988 (CI = 0.967 to 0.996) for maximum fat diameter and 0.977 (CI = 0.936 to 0.993) for the number of fat deposits. BA plots for all of four CT features between two readers are displayed in Figs. 1–4. Correlogram findings: Figure 5 shows the correlogram containing the pairwise correlation coefficients between CT features. The immaturity of tumor was more strongly correlated to the number of fat, maximum diameter of calcifications, and number of calcifications with Pearson correlation coefficients of 0.846, 0.806, and 0.682, respectively. Also, the number of fat exhibited a strong correlation to the number of calcifications and maximum diameter of calcifications with Pearson correlation coefficients of 0.901 and 0.838. Discussion Torsion, rupture, malignant transformation, and infection are frequently encountered complications of teratomas. Immune-mediated limbic encephalitis and autoimmune hemolytic anemia are less frequent ( 19 ). For immature or malignant teratomas at an early stage, surgery alone might be curative, offering a favorable prognosis for patients. However, for advanced-stage immature teratomas, a combination of surgery, chemotherapy, and potentially radiation therapy might be necessary. Therefore, it is essential to accurately diagnose teratomas at an early stage in order to ensure optimal management and prevent complications ( 20 ). The study conducted by Katlariwala et al. ( 21 ) shows a “good” level of inter-reader agreement while performing ultrasound examinations on patients with ovarian teratomas, with kappa values ranging from 0.76 to 0.89 (P < 0.001). A previous study conducted by Cao et al. ( 22 ) also discovered the same level of US inter-reader agreement. However, it is essential to consider that there is a clear association between ultrasound experience and the reproducibility of US measurements. Faschingbauer et al. ( 23 ) demonstrated that the experienced sonologist group achieved the best diagnostic performance along with the lowest interobserver variability. While CT scans have proven to have excellent diagnostic performance in detecting ovarian teratomas, with a definitive diagnosis achieved in 98% of instances ( 6 , 24 ), there is still a substantial lack of information regarding their reproducibility. To our knowledge, there have been no previous studies that have assessed the level of inter-reader agreement when it comes to measuring ovarian teratomas using CT scans. In our study, we conducted a type 1 ICC analysis to evaluate the agreement between readers who interpret CT images of teratomas. The Type 1 ICC enables the generalizability of our findings to real-world clinical settings involving other ovarian teratoma patients who are receiving CT scans. Another key advantage of our study is that we avoided dividing our quantitative data into two groups based on their median or a proposed cut point. This was done to mitigate the possibility of information loss. By maintaining the complete spectrum of our data, we were able to identify subtle distinctions that a binary classification could mask. In addition, a semi-automated tool is used to determine the region of interest (ROI) in order to reduce the variability when defining ROIs. The process was led by the expertise of gynecologists and radiologists, while the program provided consistency and minimized the potential for human errors that could arise from manual drawing. The results of our study on radiologist agreement in evaluating ovarian teratomas using CT demonstrated an overall "excellent" level of interpretation magnitude for all four measures of maximum diameter and number of calcifications, as well as maximum diameter and number of fats. Based on these findings, we anticipate the examiners to produce consistent and similar results for these CT features when evaluating the same patient. The highest level of agreement was seen for the maximum diameter of calcification, followed by the number of calcifications, maximum diameter of fats, and number of fats. When comparing CT repeatability based on our finding with US and MRI ( 25 ) agreement that had been examined by prior research we observed a potential benefit for CT scans. CT scans might provide a more standardized and reproducible technique, leading to more accurate assessments of ovarian teratomas. The BA plots depicted in Figs. 1–4 demonstrate a clear pattern of high agreement among raters when assessing the size and number of calcifications and fat in benign tumors as suggested by ICC values. The plots also indicate that raters were able to obtain more reliable assessments for smaller benign tumors that had fewer calcifications and fats. Furthermore, the correlogram in Fig. 5 indicates that the teratomas' maturity, as indicated by a benign classification, exhibited strong positive correlations with the number of fat deposits (r = 0.846), the maximum diameter of calcifications (r = 0.806), and a moderate positive correlation with the number of calcifications (r = 0.682). This implies that the likelihood of a benign tumor is correlated with an increase in the presence and extent of fat and calcifications. The vice versa applies to the probability of malignancy presence. Additionally, a strong positive correlation was observed between the number of fat deposits and both the numbers (0.901) and maximum diameter (0.838) of calcifications. This suggests that tumors exhibiting a greater number of fat deposits also tend to show a higher number and larger size of calcifications. There were some limitations in our investigation: (( 1 )) Ovarian teratomas are rare, with malignant teratomas occurring in less than 1% of all teratomas and accounting for about 5% of all ovarian malignancies ( 26 ). The sample size of our study reflects this issue. A larger sample size could potentially provide more precise estimates of inter-reader agreement. Also, a higher number of readers could enhance the generalizability of the findings. (( 2 )) US and MRI are the framework of the Ovarian Imaging Reporting And Data System (O-RADS) classification system. As CT scans are not incorporated into the O-RADS guidelines, we were unable to assess inter-reader agreement in a CT-based guideline. (( 3 )) While CT is a widely used imaging modality for ovarian germ cell tumor assessment, MRI can offer additional advantages ( 25 ). Future research could focus on the evaluation of diagnostic performance and reproducibility of MRI and conduct a direct comparison of it with CT. (( 4 )) Our readers were very experienced specialists with > 20 years of experience, which may have resulted in a potential upward bias in our repeatability results. (( 5 )) We did not assess the intra-reader repeatability, which quantifies the consistency of measurements performed by the same radiologist over a period of time. Conclusion Diagnosis of ovarian teratomas remains a challenge, leading to the investigation of various imaging modalities. Our study addressed this challenge by demonstrating excellent inter-reader agreement for CT-based measurements of ovarian teratomas indicating that CT interpretation should in the same results. Furthermore, the strong correlations observed between tumor maturity and features like fat content and calcification suggest their potential as potential indicators of benign teratomas. Integration of CT into the diagnostic protocol for patients with ovarian teratoma could optimize reporting systems and guide clinical decision-making. Declarations Funding: None Conflict of interest: None Informed consent: All participants provided written informed consent. Animal study: N/A Acknowledgment: We would like to acknowledge all those who contributed to gathering the dataset. References Höhn AK, Brambs CE, Hiller GGR, May D, Schmoeckel E, Horn LC (2021) 2020 WHO Classification of Female Genital Tumors. 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J Comput Assist Tomogr 9900: 10.1097/RCT.0000000000001629 Dos Santos L, Mok E, Iasonos A, Park K, Soslow RA, Aghajanian C et al (2007) Squamous cell carcinoma arising in mature cystic teratoma of the ovary: a case series and review of the literature. Gynecol Oncol 105(2):321–324 Additional Declarations The authors declare no competing interests. Supplementary Files AppendixA.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8262082","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554158162,"identity":"d88d03bc-3fcb-45ff-9f51-62f0cd06043d","order_by":0,"name":"Saeed Mohammadzadeh","email":"","orcid":"","institution":"School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Mohammadzadeh","suffix":""},{"id":554158163,"identity":"78e741cd-bd96-43a0-9a55-824b314a552d","order_by":1,"name":"Alisa 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06:58:30","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65384,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/1ccba2f6a46f1cf710145570.html"},{"id":97654141,"identity":"2f5f6b77-edb0-4057-a0ad-5493f8d4d330","added_by":"auto","created_at":"2025-12-08 06:58:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165226,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1. Maximum diameter of calcifications Bland-Altman (BA) plot of readers 1 \u0026amp; 2.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/5b5a615d6275666f62dfd7c9.png"},{"id":97672807,"identity":"632467a9-30c4-452c-9549-27ce7923913d","added_by":"auto","created_at":"2025-12-08 09:38:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168989,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2. The number of calcifications BA plot of readers 1 \u0026amp; 2.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/333ba8c2175d1c7a397f662f.png"},{"id":97674665,"identity":"18fe7c4c-ddd7-4185-90cf-3e49fff41140","added_by":"auto","created_at":"2025-12-08 09:43:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157387,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. Maximum diameter of fat BA plot of readers 1 \u0026amp; 2.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/357d8fc0d032088827bd710c.png"},{"id":97673172,"identity":"fdbd6db6-90e3-4c90-bfcd-6f205b059a66","added_by":"auto","created_at":"2025-12-08 09:39:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":166210,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4. The number of fat BA plot of readers 1 \u0026amp; 2.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/f154e78f1d169c69e8dd0b2d.png"},{"id":97654147,"identity":"f78744d5-6ed2-49a1-ab3a-e1ac79e9735f","added_by":"auto","created_at":"2025-12-08 06:58:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202443,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. The Correlogram displays pairwise correlation coefficients between CT features\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/01f06a4a0b9089b8eae23908.png"},{"id":97678896,"identity":"45e7f7b6-4ef0-430d-afd9-db79d4180d8e","added_by":"auto","created_at":"2025-12-08 09:56:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1268132,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/ac58a8d1-a920-49cf-8745-e598a75bcd12.pdf"},{"id":97654142,"identity":"6ba2719a-aa03-4e4f-9815-642b5803ef87","added_by":"auto","created_at":"2025-12-08 06:58:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12899,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8262082/v1/622eb55f4521d89275cdff69.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComputed Tomography Features Show Excellent Inter-radiologist Reproducibility and Malignancy Grading for Ovarian Teratomas.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMature teratoma, often referred to as mature cystic teratoma (MCT), consists solely of fully developed tissues originating from two or three germ layers, specifically ectoderm, mesoderm, and endoderm. It is the most common ovarian tumor in children and adolescents, accounting for over 50% of occurrences in females under the age of 20 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, it can be observed in women of all ages (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The patients may exhibit no symptoms, or they may experience chronic pelvic pain or the presence of a pelvic mass. The tumors are typically discovered coincidentally during imaging taken for other purposes, as almost 20% of patients do not exhibit any symptoms after MCT is detected (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe most typical ultrasound (US) image of an MCT is an echogenic nodule containing echogenic sebaceous material and calcifications (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The US has a sensitivity ranging from 58% to 92.7% and a specificity ranging from 87.5% to 99% for the diagnosis of adult teratomas. However, US may lead to misdiagnosis of teratomas in the presence of other pelvic malignancies, including fibromas and endometriosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTumors that cannot be definitively identified in the US can be further assessed using computed tomography (CT). CT has a higher diagnostic performance than the US in detecting MCTs due to its superior capability in detecting intratumoral fat and calcifications, resulting in a sensitivity of 93% to 98% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The occurrence of teeth within the Rokitansky nodule is characteristic of fully developed teratoma. Also, curvilinear calcifications might be observed within the tumor's septa or wall (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The quantity of fat present in immature teratoma varies. Specifically, certain tumors may appear on imaging with very little or no discernible fat. The cystic fluid in immature teratomas has a lower fat content compared to the sebaceous fluid in MCTs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, the CT attenuation of the fluid within young teratoma is frequently more than that of its mature equivalent, resembling the attenuation of simple fluid (0\u0026ndash;10 HU) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThroughout clinical practice, images are interpreted by radiologists who possess different levels of expertise. Assessing the collective opinion of readers is crucial, particularly when it influences the differential diagnosis and, subsequently, patient medical treatment. The objective of our study was to examine the concordance among various readers when utilizing CT images to evaluate the tumors of ovarian teratoma in order to optimize reporting systems and guide clinical decision-making.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eThe Handbook of Inter-Rater Reliability (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) has been used as the basis for the methodology and analysis in this investigation. Also, we adhered to the principles outlined in the Declaration of Helsinki. The Institutional Review Board of the University of Fukui Hospital (approval number: 20210730) approved the study. Patients supplied written informed consent to participate in the study and were offered an opt-out option. Anonymized clinical data were utilized. Participants were retrospectively enrolled from a dataset titled as \u0026ldquo;Evaluation of calcification distribution by CT-based textural analysis for discrimination of immature teratoma\u0026rdquo; from Figshare (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The study protocol has been registered a priori at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/n6fw5/\u003c/span\u003e\u003cspan address=\"https://osf.io/n6fw5/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on the Open Science Framework (OSF) platform (Appendix A).\u003c/p\u003e\u003cp\u003ePatient selection:\u003c/p\u003e\u003cp\u003eWe retrospectively examined the medical records of 32 patients from Fukui Hospital who were recruited between January 2008 and June 2021 and had histopathologically confirmed MCT (n\u0026thinsp;=\u0026thinsp;28) and embryonic teratomas (n\u0026thinsp;=\u0026thinsp;4). Ten patients with abdominal CT data were referred from other institutions, making this study a multicentral investigation. The study included patients who were under the age of 20, received transabdominal ultrasonography and/or abdominal and pelvic CT scans prior to surgery, and underwent surgery at our institution. In addition, tumor characteristics such as maximum diameter (cm), mean diameter (cm), volume (cm3) as well as blood level of alpha-fetoprotein (AFP), CA125, CA19-9 were collected.\u003c/p\u003e\u003cp\u003eImage interpretation:\u003c/p\u003e\u003cp\u003eA 64-slice multidetector CT scanner (Discovery CT750HD; GE Medical Systems, Milwaukee, WI, USA) was used to conduct abdominal and pelvic non-contrast CT exams. The analysis of images was performed using the picture archiving and communication system, and Ziostation2 software (Ziosoft, Tokyo, Japan) was utilized to create a three-dimensional image. The CT images were retrospectively interpreted by a gynecologic oncologist and a board-certified radiologist, both having extensive experience of 21 and 24 years, respectively. Both specialists have double certifications and specialize in gynecological imaging. The images were assessed for the following specifications: a) maximum diameter of calcifications (cm), b) number of calcifications, c) maximum diameter of fats (cm), and d) number of fats. The cut point for identifying calcified lesions was established as a CT density of 130 Hounsfield units, while fat was defined by a CT density ranging from \u0026minus;\u0026thinsp;144 to -20 Hounsfield units (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Both readers were blinded to the outcomes of the other imaging examinations, clinical data, and histological findings.\u003c/p\u003e\u003cp\u003eTexture analysis:\u003c/p\u003e\u003cp\u003eThe CT images of the patients were extracted from the picture archiving and communication system in DICOM format and sent to the LIFEx software version 7.2.0 (LITO, CEA, Inserm, CNRS, Univ. Paris-Sud, Universit\u0026eacute; Paris Saclay) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The procedure was carried out independently by a gynecologic oncologist and a board-certified radiologist who were blinded to the tumor diagnosis. This was done to minimize any bias when assessing the radiomic features. They meticulously outlined each target lesion along its contour and extracted calcifications and fats manually. Prior to calculating radiomic features, the voxel intensities were resampled using the relative resampling approach, employing a fixed bin number of 128.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis:\u003c/h2\u003e\u003cp\u003eThe statistical analyses were performed using Stata 17.0 and Medcalc 22.0. To assess the agreement of quantitative variables, we computed the intraclass correlation coefficient (ICC) type 1. The findings of type 1 ICC can be generalized from our specific patient sample to the broader patient population. We defined agreement classification as poor when ICC was \u0026lt;\u0026thinsp;0.50, moderate when 0.50\u0026ndash;0.75, good when 0.75\u0026ndash;0.90, and excellent when \u0026ge;\u0026thinsp;0.90 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, Bland-Altman (BA) graphs were generated to evaluate the visual interpretation of the agreement further. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was determined to have statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics:\u003c/p\u003e\u003cp\u003eA total of 32 female patients were involved in our database. The mean age of patients was 14.5 (ranging from 6 to 19). Of these patients, 28 (87.5%) presented with MCT. The remaining 4 patients (12.5%) had immature teratomas. These immature teratomas included 2 cases of grade 1, 1 case of grade 2, and 1 case of grade 3. In addition, 26 patients (81.2%) experienced symptoms such as stomach pain and distension, whereas the remaining 6 patients (18.8%) were asymptomatic. A summary of the imaging features and clinical data of the patients in regard to tumor malignancy status is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003echaracteristics of included patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eimmature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emature\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.8 (16.9\u0026ndash;21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.1 (15.3\u0026ndash;25.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 (\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.5 (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum diameter (cm)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.5 (18.0\u0026ndash;25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.3 (3.0\u0026ndash;27.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean diameter (cm)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.8 (14.3\u0026ndash;17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.7 (2.7\u0026ndash;18.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolume (cm3)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3451 (2660\u0026ndash;4320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e969.7 (18.8\u0026ndash;5049)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (78.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFP (ng/mL)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.7 (3.8\u0026ndash;185.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1 (0.6\u0026ndash;5.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA19-9 (U/mL)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e406.3 (24.8\u0026ndash;1438)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.6 (3.6\u0026ndash;1350)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA125 (U/mL)\u003c/p\u003e\u003cp\u003eMeans (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e289.4 (30.6\u0026ndash;885.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.2 (8.8\u0026ndash;173.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCT scan inter-reader agreement:\u003c/p\u003e\u003cp\u003eWe assessed the level of agreement between two readers in measuring calcifications and fat content within the structures using ICC. Only 14 patients with non-contrast CT were included in this analysis. The ICC for the maximum diameter of calcifications and the number of calcifications were 0.994 (CI\u0026thinsp;=\u0026thinsp;0.980 to 0.998) and 0.991 (CI\u0026thinsp;=\u0026thinsp;0.975 to 0.997), respectively. Similarly, high agreement was observed for fat measurements, with an ICC of 0.988 (CI\u0026thinsp;=\u0026thinsp;0.967 to 0.996) for maximum fat diameter and 0.977 (CI\u0026thinsp;=\u0026thinsp;0.936 to 0.993) for the number of fat deposits. BA plots for all of four CT features between two readers are displayed in Figs.\u0026nbsp;1\u0026ndash;4.\u003c/p\u003e\u003cp\u003eCorrelogram findings:\u003c/p\u003e\u003cp\u003eFigure 5 shows the correlogram containing the pairwise correlation coefficients between CT features. The immaturity of tumor was more strongly correlated to the number of fat, maximum diameter of calcifications, and number of calcifications with Pearson correlation coefficients of 0.846, 0.806, and 0.682, respectively. Also, the number of fat exhibited a strong correlation to the number of calcifications and maximum diameter of calcifications with Pearson correlation coefficients of 0.901 and 0.838.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTorsion, rupture, malignant transformation, and infection are frequently encountered complications of teratomas. Immune-mediated limbic encephalitis and autoimmune hemolytic anemia are less frequent (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). For immature or malignant teratomas at an early stage, surgery alone might be curative, offering a favorable prognosis for patients. However, for advanced-stage immature teratomas, a combination of surgery, chemotherapy, and potentially radiation therapy might be necessary. Therefore, it is essential to accurately diagnose teratomas at an early stage in order to ensure optimal management and prevent complications (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The study conducted by Katlariwala et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) shows a \u0026ldquo;good\u0026rdquo; level of inter-reader agreement while performing ultrasound examinations on patients with ovarian teratomas, with kappa values ranging from 0.76 to 0.89 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A previous study conducted by Cao et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) also discovered the same level of US inter-reader agreement. However, it is essential to consider that there is a clear association between ultrasound experience and the reproducibility of US measurements. Faschingbauer et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) demonstrated that the experienced sonologist group achieved the best diagnostic performance along with the lowest interobserver variability. While CT scans have proven to have excellent diagnostic performance in detecting ovarian teratomas, with a definitive diagnosis achieved in 98% of instances (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), there is still a substantial lack of information regarding their reproducibility. To our knowledge, there have been no previous studies that have assessed the level of inter-reader agreement when it comes to measuring ovarian teratomas using CT scans.\u003c/p\u003e\u003cp\u003eIn our study, we conducted a type 1 ICC analysis to evaluate the agreement between readers who interpret CT images of teratomas. The Type 1 ICC enables the generalizability of our findings to real-world clinical settings involving other ovarian teratoma patients who are receiving CT scans. Another key advantage of our study is that we avoided dividing our quantitative data into two groups based on their median or a proposed cut point. This was done to mitigate the possibility of information loss. By maintaining the complete spectrum of our data, we were able to identify subtle distinctions that a binary classification could mask. In addition, a semi-automated tool is used to determine the region of interest (ROI) in order to reduce the variability when defining ROIs. The process was led by the expertise of gynecologists and radiologists, while the program provided consistency and minimized the potential for human errors that could arise from manual drawing.\u003c/p\u003e\u003cp\u003eThe results of our study on radiologist agreement in evaluating ovarian teratomas using CT demonstrated an overall \"excellent\" level of interpretation magnitude for all four measures of maximum diameter and number of calcifications, as well as maximum diameter and number of fats. Based on these findings, we anticipate the examiners to produce consistent and similar results for these CT features when evaluating the same patient. The highest level of agreement was seen for the maximum diameter of calcification, followed by the number of calcifications, maximum diameter of fats, and number of fats. When comparing CT repeatability based on our finding with US and MRI (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) agreement that had been examined by prior research we observed a potential benefit for CT scans. CT scans might provide a more standardized and reproducible technique, leading to more accurate assessments of ovarian teratomas. The BA plots depicted in Figs.\u0026nbsp;1\u0026ndash;4 demonstrate a clear pattern of high agreement among raters when assessing the size and number of calcifications and fat in benign tumors as suggested by ICC values. The plots also indicate that raters were able to obtain more reliable assessments for smaller benign tumors that had fewer calcifications and fats. Furthermore, the correlogram in Fig.\u0026nbsp;5 indicates that the teratomas' maturity, as indicated by a benign classification, exhibited strong positive correlations with the number of fat deposits (r\u0026thinsp;=\u0026thinsp;0.846), the maximum diameter of calcifications (r\u0026thinsp;=\u0026thinsp;0.806), and a moderate positive correlation with the number of calcifications (r\u0026thinsp;=\u0026thinsp;0.682). This implies that the likelihood of a benign tumor is correlated with an increase in the presence and extent of fat and calcifications. The vice versa applies to the probability of malignancy presence. Additionally, a strong positive correlation was observed between the number of fat deposits and both the numbers (0.901) and maximum diameter (0.838) of calcifications. This suggests that tumors exhibiting a greater number of fat deposits also tend to show a higher number and larger size of calcifications.\u003c/p\u003e\u003cp\u003eThere were some limitations in our investigation: ((\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)) Ovarian teratomas are rare, with malignant teratomas occurring in less than 1% of all teratomas and accounting for about 5% of all ovarian malignancies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The sample size of our study reflects this issue. A larger sample size could potentially provide more precise estimates of inter-reader agreement. Also, a higher number of readers could enhance the generalizability of the findings. ((\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)) US and MRI are the framework of the Ovarian Imaging Reporting And Data System (O-RADS) classification system. As CT scans are not incorporated into the O-RADS guidelines, we were unable to assess inter-reader agreement in a CT-based guideline. ((\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)) While CT is a widely used imaging modality for ovarian germ cell tumor assessment, MRI can offer additional advantages (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Future research could focus on the evaluation of diagnostic performance and reproducibility of MRI and conduct a direct comparison of it with CT. ((\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)) Our readers were very experienced specialists with \u0026gt;\u0026thinsp;20 years of experience, which may have resulted in a potential upward bias in our repeatability results. ((\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)) We did not assess the intra-reader repeatability, which quantifies the consistency of measurements performed by the same radiologist over a period of time.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDiagnosis of ovarian teratomas remains a challenge, leading to the investigation of various imaging modalities. Our study addressed this challenge by demonstrating excellent inter-reader agreement for CT-based measurements of ovarian teratomas indicating that CT interpretation should in the same results. Furthermore, the strong correlations observed between tumor maturity and features like fat content and calcification suggest their potential as potential indicators of benign teratomas. Integration of CT into the diagnostic protocol for patients with ovarian teratoma could optimize reporting systems and guide clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003cp\u003eConflict of interest: None\u003c/p\u003e\u003cp\u003e Informed consent: All participants provided written informed consent.\u003c/p\u003e\u003cp\u003eAnimal study: N/A\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge all those who contributed to gathering the dataset.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eH\u0026ouml;hn AK, Brambs CE, Hiller GGR, May D, Schmoeckel E, Horn LC (2021) 2020 WHO Classification of Female Genital Tumors. Geburtshilfe Frauenheilkd 81(10):1145\u0026ndash;1153\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSurti U, Hoffner L, Chakravarti A, Ferrell RE (1990) Genetics and biology of human ovarian teratomas. I. Cytogenetic analysis and mechanism of origin. Am J Hum Genet 47(4):635\u0026ndash;643\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyhan A, Bukulmez O, Genc C, Karamursel BS, Ayhan A (2000) Mature cystic teratomas of the ovary: case series from one institution over 34 years. Eur J Obstet Gynecol Reprod Biol 88(2):153\u0026ndash;157\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaleh M, Bhosale P, Menias CO, Ramalingam P, Jensen C, Iyer R et al (2021) Ovarian teratomas: clinical features, imaging findings and management. Abdom Radiol (New York) 46(6):2293\u0026ndash;2307\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuinn SF, Erickson S, Black WC (1985) Cystic ovarian teratomas: the sonographic appearance of the dermoid plug. Radiology 155(2):477\u0026ndash;478\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaba L, Guerriero S, Sulcis R, Virgilio B, Melis G, Mallarini G (2009) Mature and immature ovarian teratomas: CT, US and MR imaging characteristics. Eur J Radiol 72(3):454\u0026ndash;463\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMais V, Guerriero S, Ajossa S, Angiolucci M, Paoletti AM, Melis GB (1995) Transvaginal ultrasonography in the diagnosis of cystic teratoma. Obstet Gynecol 85(1):48\u0026ndash;52\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedman AC, Pyatt RS, Hartman DS, Downey EF Jr., Olson WB (1982) CT of benign cystic teratomas. AJR Am J Roentgenol 138(4):659\u0026ndash;665\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrammer HM 3rd, Buck JL, Hayes WS, Sheth S, Tavassoli FA (1990) From the archives of the AFIP. Malignant germ cell tumors of the ovary: radiologic-pathologic correlation. Radiographics: Rev publication Radiological Soc North Am Inc 10(4):715\u0026ndash;724\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUeno T, Tanaka YO, Nagata M, Tsunoda H, Anno I, Ishikawa S et al (2004) Spectrum of germ cell tumors: from head to toe. Radiographics: Rev publication Radiological Soc North Am Inc 24(2):387\u0026ndash;404\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamaoka T, Togashi K, Koyama T, Fujiwara T, Higuchi T, Iwasa Y et al (2003) Immature teratoma of the ovary: correlation of MR imaging and pathologic findings. Eur Radiol 13(2):313\u0026ndash;319\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGwet KL (2021) Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement Among Raters: Vol 2: Analysis of Quantitative Ratings. Advanced Analytics, LLC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGwet KL (2021) Handbook of Inter-Rater Reliability: Volume 1: Analysis of Categorical Ratings. Advanced Analytics, LLC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakamori A, Tsuyoshi H, Tsujikawa T, Orisaka M, Kurokawa T, Yoshida Y (2023) Additional file 1 of Evaluation of calcification distribution by CT-based textural analysis for discrimination of immature teratoma. figshare\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr., Detrano R (1990) Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15(4):827\u0026ndash;832\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuinet C, Ghossain MA, Buy JN, Malbec L, Hugol D, Truc JB et al (1995) Mature cystic teratomas of the ovary: CT and MR findings. Eur J Radiol 20(2):137\u0026ndash;143\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNioche C, Orlhac F, Boughdad S, Reuz\u0026eacute; S, Goya-Outi J, Robert C et al (2018) LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer Res 78(16):4786\u0026ndash;4789\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoo TK, Li MY (2016) A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15(2):155\u0026ndash;163\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBirbas E, Kanavos T, Gkrozou F, Skentou C, Daniilidis A, Vatopoulou A (2023) Ovarian Masses in Children and Adolescents: A Review of the Literature with Emphasis on the Diagnostic Approach. Child (Basel Switzerland). ;10(7)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdhikari S, Joti S, Chhetri PK (2022) Paediatric Ovarian Dysgerminoma: A Case Report. JNMA 60(255):985\u0026ndash;988\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatlariwala P, Wilson MP, Pi Y, Chahal BS, Croutze R, Patel D et al (2022) Reliability of ultrasound ovarian-adnexal reporting and data system amongst less experienced readers before and after training. World J Radiol 14(9):319\u0026ndash;328\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao L, Wei M, Liu Y, Fu J, Zhang H, Huang J et al (2021) Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses. Gynecol Oncol 162(1):107\u0026ndash;112\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaschingbauer F, Benz M, H\u0026auml;berle L, Goecke TW, Beckmann MW, Renner S et al (2012) Subjective assessment of ovarian masses using pattern recognition: the impact of experience on diagnostic performance and interobserver variability. Arch Gynecol Obstet 285(6):1663\u0026ndash;1669\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTerzic M, Rapisarda AMC, Della Corte L, Manchanda R, Aimagambetova G, Norton M et al (2021) Diagnostic work-up in paediatric and adolescent patients with adnexal masses: an evidence-based approach. J Obstet Gynaecol 41(4):503\u0026ndash;515\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetrocelli R, Doshi A, Slywotzky C, Savino M, Melamud K, Tong A et al Performance of O-RADS MRI Score in Differentiating Benign From Malignant Ovarian Teratomas: MR Feature Analysis for Differentiating O-RADS 4 From O-RADS 2. J Comput Assist Tomogr 9900:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/RCT.0000000000001629\u003c/span\u003e\u003cspan address=\"10.1097/RCT.0000000000001629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDos Santos L, Mok E, Iasonos A, Park K, Soslow RA, Aghajanian C et al (2007) Squamous cell carcinoma arising in mature cystic teratoma of the ovary: a case series and review of the literature. Gynecol Oncol 105(2):321\u0026ndash;324\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tehran University of Medical Sciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ovarian Teratoma, Computed Tomography (CT), Calcification, Fat, Diagnostic Test Accuracy","lastPublishedDoi":"10.21203/rs.3.rs-8262082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8262082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjectives: To evaluate the reproducibilities of computed tomography (CT) features among patients with ovarian teratoma tumors.\u003c/p\u003e\n\u003cp\u003eMethods: This retrospective study included 32 patients with confirmed mature cystic teratoma and embryonic teratoma, recruited between January 2008 and June 2021. The study protocol has been pre-registered at (https://osf.io/n6fw5/) on the Open Science Framework (OSF) platform. Patients under the age of 20 who received transabdominal ultrasonography and/or abdominal and pelvic CT scans prior to surgery and underwent surgery at our institution were included. A gynecologic oncologist and a board-certified radiologist, both having extensive experience, interpreted CT scans for the following specifications: a) maximum diameter of calcifications (cm), b) number of calcifications, c) maximum diameter of fats (cm), and d) number of fats.\u003c/p\u003e\n\u003cp\u003eResults: The intraclass correlation coefficient (ICC) for maximum diameter of calcifications, number of calcifications, maximum fat diameter, and number of fat were 0.994 (CI = 0.980 to 0.998), 0.991 (CI = 0.975 to 0.997), 0.988 (CI = 0.967 to 0.996), and 0.977 (CI = 0.936 to 0.993), respectively. Also, the maturity of the tumor was more strongly correlated to the number of fat, the maximum diameter of calcifications, and the number of calcifications, with Pearson correlation coefficients of 0.846, 0.806, and 0.682, respectively.\u003c/p\u003e\n\u003cp\u003eConclusion: Our findings demonstrated excellent inter-reader agreement for all four CT measurements. This indicates a high consistency in CT-based evaluations of ovarian teratomas, which could enhance diagnostic accuracy and inform clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Computed Tomography Features Show Excellent Inter-radiologist Reproducibility and Malignancy Grading for Ovarian Teratomas.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 06:58:25","doi":"10.21203/rs.3.rs-8262082/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1be2411e-2347-4cab-acb7-73889ac065c6","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58971446,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2025-12-08T06:58:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 06:58:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8262082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8262082","identity":"rs-8262082","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}