{"paper_id":"4df3c3a1-2529-426c-884f-1842e276a970","body_text":"Citation: Yamanaka, S.; Kawahara,\nN.; Kawaguchi, R.; Waki, K.;\nMaehana, T.; Fukui, Y.; Miyake, R.;\nYamada, Y.; Kobayashi, H.; Kimura, F.\nThe Comparison of Three Predictive\nIndexes to Discriminate Malignant\nOvarian Tumors from Benign\nOvarian Endometrioma: The\nCharacteristics and Efficacy.\nDiagnostics 2022, 12, 1212. https://\ndoi.org/10.3390/diagnostics12051212\nAcademic Editor: Dah Ching Ding\nReceived: 14 April 2022\nAccepted: 10 May 2022\nPublished: 12 May 2022\nPublisher’s Note: MDPI stays neutral\nwith regard to jurisdictional claims in\npublished maps and institutional affil-\niations.\nCopyright: © 2022 by the authors.\nLicensee MDPI, Basel, Switzerland.\nThis article is an open access article\ndistributed under the terms and\nconditions of the Creative Commons\nAttribution (CC BY) license (https://\ncreativecommons.org/licenses/by/\n4.0/).\ndiagnostics \nArticle\nThe Comparison of Three Predictive Indexes to Discriminate\nMalignant Ovarian T umors from Benign Ovarian\nEndometrioma: The Characteristics and Efficacy\nShoichiro Yamanaka, Naoki Kawahara *\n , Ryuji Kawaguchi\n , Keita Waki, Tomoka Maehana, Yosuke Fukui,\nRyuta Miyake, Yuki Yamada, Hiroshi Kobayashi and Fuminori Kimura\nDepartment of Obstetrics and Gynecology, Nara Medical University, 840 Shijo-cho, Kashihara 634-8522, Japan;\nshoichiroyamanaka@naramed-u.ac.jp (S.Y.); kawaryu@naramed-u.ac.jp (R.K.); k178719@naramed-u.ac.jp (K.W.);\ntmaehana@naramed-u.ac.jp (T.M.); f.yosuke1002@naramed-u.ac.jp (Y.F.); ryuta-miyake@naramed-u.ac.jp (R.M.);\nyuki0528@naramed-u.ac.jp (Y.Y.); hirokoba@naramed-u.ac.jp (H.K.); kimurafu@naramed-u.ac.jp (F.K.)\n* Correspondence: naoki35@naramed-u.ac.jp; Tel.: +81-744-29-8877\nAbstract: This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian\nendometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm\n(ROMA), and the R2 predictive index. This retrospective study was conducted at the Depart-\nment of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of\n171 patients were included in the study. In the current study, cases were divided into three cohorts:\npre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor\nmainly received laparoscopic surgery, and patients with suspected malignant tumors underwent\nlaparotomy. Information from a review chart of the patients’ medical records was collected. In the\ncombined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index,\nand tumor laterality were extracted as independent factors for predicting malignant tumors (hazard\nratio (HR): 222.14, 95% confidence interval (CI): 22.27–2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90–33.13,\np < 0.001; HR: 0.15, 95% CI: 0.03–0.75, p = 0.021, respectively). In the pre-menopausal cohort, a\nmultivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as\nindependent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47–28.22, p = 0.013; HR:\n31.19, 95% CI: 8.48–114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only\nextracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43–272.52,\np < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive\nindex is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes.\nKeywords: ovarian endometrioma; endometriosis associated ovarian cancer; malignant ovarian\ntumor; borderline ovarian tumor; CPH index; ROMA index; R2 predictive index\n1. Introduction\nOvarian cancer is the fifth leading cause of cancer-related death in women [1]. This\ndisease cannot be diagnosed in the early stages and is called the silent killer [2–4]. As such,\nmost ovarian cancer cases are diagnosed at advanced stages [5–7], and over 185,000 deaths\ndue to this disease are reported annually worldwide [8,9].\nMolecular genetics and morphologic characteristics revealed that ovarian cancer can\nbe divided into two categories, designated types 1 and 2 [ 10–12]. Type 1 tumors show\na stepwise progression (adenoma–carcinoma sequence), which comprise endometriosis-\nassociated ovarian cancer (EAOC), such as clear cell carcinoma and low-grade endometrioid\ncarcinoma, as well as mucinous carcinoma and low-grade serous carcinoma [13,14]. Type 2\ntumors range from the normal epithelium to precursor lesions, and finally to high-grade\nserous and endometrioid carcinoma, malignant mixed mesodermal tumors (carcinosarco-\nmas), and undifferentiated carcinoma [13,15]. The former shows low progression but is\nDiagnostics 2022, 12, 1212. https://doi.org/10.3390/diagnostics12051212 https://www.mdpi.com/journal/diagnostics\n\nDiagnostics 2022, 12, 1212 2 of 16\nresistant to chemotherapy; in contrast, the latter is highly progressive but shows vulner-\nability to chemotherapy [16]. In type 1 ovarian cancer, most EAOC arises from ovarian\nendometriosis [17–19], and there is a major challenge for physicians in the case of early\ndetection/surgical treatment and effects on fertility.\nOvarian endometriosis is defined as the presence of endometrial glands and stroma\noutside of the uterus, and it is most often detected in the pelvic peritoneum and ovaries [20].\nRepeated hemorrhages in the peritoneum or ovaries may contribute to the symptoms\nof dysmenorrhea [21,22], chronic pelvic pain [ 23,24], and infertility [ 25,26], which nega-\ntively affect the patients’ quality of life. There is also evidence of an epidemiologic link\nbetween iron overload and the various types of human carcinoma, including malignant\nmesothelioma, renal cell carcinoma, hepatocellular carcinoma, and EAOC [ 27–31]. We\nshowed that total iron levels of cyst fluid can discriminate EAOC from ovarian endometri-\noma (OE), with a cutoff point of 64.8 mg/L (sensitivity, 85%; specificity, 98%) [ 32]; and\nmagnetic resonance (MR) relaxometry, which can noninvasively measure cyst fluid iron\nconcentration, can discriminate with a cutoff point of 12.1 (sensitivity, 86%; specificity,\n94%) [33,34]. Moreover, we showed a novel predictive tool in the R2 predictive index,\nwhich requires tumor diameter (mm) and blood tumor marker as CEA (ng/mL). This index\nis useful and valuable for the detection of the malignant transformation of endometrioma\n(i.e., EAOC), with good accuracy (sensitivity, 82%; specificity, 68%) [35]. In clinical practice,\nultrasound is the most powerful tool to detect ovarian tumors and can differentiate between\nOE and malignant ovarian tumors (i.e., IOTA classification) [36,37]; however, a good level\nof understanding and training are needed to score the system. There are some effective\ntools to discriminate malignant ovarian tumors from benign tumors [35–43]. The risk of\novarian malignancy algorithm (ROMA) index value is an algorithm that takes into account\nthe levels of carbohydrate antigen125 (CA125) and human epididymis protein 4 (HE4),\ntogether with menopausal status, using quantitative and objective parameters; and the\nCopenhagen (CPH) index takes into account HE4, CA125, and age, rather than menopausal\nstatus, with different definitions.\nThe current study aimed to compare the efficacy of these predictive tools and investi-\ngate the characteristics of these indexes.\n2. Materials and Methods\n2.1. Patients\nA list of patients with primary, previously untreated, histologically-confirmed ovarian\ntumors who were treated at Nara Medical University Hospital between January 2008 and\nJuly 2021 was generated from our institutional registry. We retrospectively included in this\nstudy the following cases of OE as benign ovarian tumor and EAOC cases as malignant\ntumor with available blood samples for tumor marker calculations. All of the OE and\nEAOC cases were histologically confirmed. Written consent for the use of the patients’\nclinical data for research was obtained at the first hospitalization, and after approval\nby the Ethics Review Committee of the Nara Medical Hospital; the opt-out form was\nprovided through our institutional homepage. The current study consisted of three cohorts:\nthe pre-menopausal, post-menopausal, and combined cohorts. Pre-menopause and post-\nmenopause were divided by age, namely under 50 years old was defined as pre-menopause\nand over 50 years old as post-menopause. The pre-menopausal cohort included 115 patients\nwith newly diagnosed ovarian tumors. A total of 56 patients were included in the post-\nmenopause cohort. No patients had undergone chemotherapy or radiotherapy for the\novarian tumors prior to treatment. Patients with OE mainly received laparoscopic surgery,\nand the patients suspected of harboring malignant tumors underwent laparotomy. The\nfollowing factors were collected through a chart review of the patients’ medical records: age;\nbody mass index (BMI); parity; postoperative diagnosis, including FIGO (The International\nFederation of Gynecology and Obstetrics) stage; the date of surgery; tumor diameter;\nmenopausal status; and pre-treatment blood test results, including CA125, carbohydrate\n\nDiagnostics 2022, 12, 1212 3 of 16\nantigen 19-9 (CA 19-9), carcinoembryonic antigen (CEA), and HE4 as a tumor marker. The\ncases shared with a previous study [35] were 72 cases (42.1%).\n2.2. T umor Imaging and Diagnoses\nAll patients first visited the outpatient clinic and underwent internal examination,\nincluding ultrasound followed by routine MR imaging using T1W and T2W sequences.\nTumor diameter was recorded as the largest diameter among axial, sagittal, and coronal\nimaging. Patients were largely diagnosed with OE or EAOC by MRI, and this was con-\nfirmed by the histological examination using the surgically removed tumors by at least\ntwo pathologists who were blinded to the study. The number of EAOC cases that were\nhistologically proven as arising from endometriosis were 41 cases (54.7%).\n2.3. Detection of CA125, CA19-9, CEA, and HE4 Concentrations\nSamples were collected from all the patients prior to surgery using blood collection\ntubes without anticoagulants. Each blood sample was centrifuged at 3000 rpm and stored\nat −80 ◦C until use. Tumor markers including CA125 (ARCHITECT CA125 II, Abbott Japan\nLLC, Tokyo, Japan), CA19-9 (CL AIA-PACK® SLa, Tosoh Corporation, Tokyo, Japan), CEA\n(CL AIA-PACK® CEA, Tosoh Corporation, Tokyo, Japan), and HE4 (ARCHITECT HE4,\nAbbott Japan LLC, Tokyo, Japan) were measured using a chemiluminescence immunoassay,\naccording to the manufacturer’s instructions. Serum samples in dry ice were transported to\nthe Tosoh diagnostics product divisions (Tosoh Corporation, Kanagawa, Japan), and CA19-\n9 and CEA concentrations were determined immediately. HE4 and CA125 (ARCHITECT\nCA125 II) were measured at BML INC., Tokyo, Japan. In case of CA125 and HE4 levels\nunder the limit, we recorded the lower limit of calibration as 1 (U/mL) and 20 (pmol/L),\nrespectively. Measurements were performed by clinical laboratory technologists who were\nblinded to the study.\n2.4. Calculation of the ROMA, the CPH, and the R2 Predictive Value\nUsing the concentrations of CA125, HE4, and CEA, we calculated the Copenhagen\n(CPH) index, the risk of ovarian malignancy algorithm (ROMA) index, and the R2 predictive\nindex, according to the mathematical equations presented below.\nThe ROMA index was calculated using the following equations [44]:\nPre-menopausal predictive index (PI) = -12.0 + 2.38 × LN(HE4) + 0.0626 × LN(CA 125)\nPost-menopausal PI = −8.09 + 1.04 × LN(HE4) + 0.732 × LN(CA125)\nROMA(%) = exp(PI)/[1 + exp(PI)] × 100\n(1)\nLN = natural log function and exp(PI) = ePI.\nThe CPH index was calculated using the following equations [45]:\nPI = −14.0647 + 1.0649 × log2(HE4) + 0.6050 × log2(CA125) + 0.2672 × (age/10)\nCPH-I = exp(PI)/[1 + exp(PI)] × 100 (2)\nThe R2 predictive index was calculated using the following equations [35]:\n[R2 predictive index] = 27.27 − 7.90 × 10−2 × (Tumor diameter) − 1.31 × (CEA) (3)\n2.5. Statistical Analysis\nAnalyses were performed using SPSS version 25.0 (IBM SPSS, Armonk, NY, USA). The\ndifferences of each factor, including the CPH index, the ROMA index, and the R2 predictive\nindex among groups, were compared using a Mann–Whitney U test or Kruskal–Wallis\none-way ANOVA test. The receiver operating characteristic (ROC) curve analysis was\nperformed to determine the cut-off value for predicting malignant ovarian tumors in each\npre-menopausal, post-menopausal, and combined (pre- and post-menopause) cohort. The\n\nDiagnostics 2022, 12, 1212 4 of 16\ncut-off value was based on the highest Youden index (i.e., sensitivity + specificity − 1).\nWe next used a logistic regression analysis to assess the risk factors for malignant ovarian\ntumors (i.e., EAOC). A two-sided p < 0.05 was considered as indicating a statistically\nsignificant difference.\n3. Results\n3.1. Patients\nFrom January 2008 to July 2021, a total of 171 patients included in this study were\ndivided as follows: 115 patients who were under 50 years old as the pre-menopausal\ncohort, and 56 patients over 50 years old as the post-menopausal cohort. The combined\ncohort consisted of the pre- and post-menopausal cohorts. The demographic and clinical\ncharacteristics of the combined cohort are outlined in Table 1. In the combined cohort,\na post-operative diagnosis of OE was found in 96 (56.1%) and malignant tumors in 75\n(43.9%) patients, including eight cases of borderline tumor. In this cohort, there was\nsignificant differentiation in age, BMI, gravida, parity, cyst size, menopausal status, and\ntumor laterality. Table 2 shows the distribution of each biological marker. CEA, HE4,\nCA125, and D-dimer reached significant differentiation between a benign tumor and\nmalignant tumor.\nTable 1. Demographic and clinical characteristics of the combined cohort.\nBenign T umor (OE) Malignant T umor (EAOC) p-Value\nNumber n = 96 n = 75\nAge (years)\nMedian (range) 37.00 (18–63) 54.00 (21–82)\nMean ± SD 36.40 ± 8.82 54.36 ± 11.63 <0.001\nBMI\nMedian (range) 20.05 (14.52–34.25) 21.98 (15.20–36.00)\nMean ± SD 20.75 ± 3.55 22.49 ± 4.22 0.002\nGravida\n0 55 25\n≥1 41 50 0.001\nParity\n0 59 26\n≥1 37 49 <0.001\nFIGO sage – I (n = 49), II (n = 3), III (n = 15),\nIV (n = 8)\nSubtype Endometrioma ( n = 96) Endometrioid carcinoma\n(n = 27)\nCCC (n = 40)\nSMBT (n = 8)\nCyst size (mm)\nMedian (range) 64.50 (38.00–185.00) 105.00 (16.50–350.00)\nMean ± SD 67.79 ± 22.97 110.05 ± 60.85 <0.001\nMenopause\nYes 5 51\nNo 91 24 <0.001\nLaterality *\nUnilateral 56 60\nBilateral 40 14 0.001\nOE ovarian endometrioma, EAOC endometriosis-associated ovarian cancer, BMI body mass index, FIGO The\nInternational Federation of Gynecology and Obstetrics, CCC clear cell carcinoma, SMBT seromucinous borderline\ntumor. * missing data.\n\nDiagnostics 2022, 12, 1212 5 of 16\nTable 2. Tumor markers in blood samples in the combined cohort.\nBenign T umor (OE) Malignant T umor (EAOC) p-Value\nNumber n = 96 n = 75\nCA 19-9 (U/mL)\nMedian (range) 23.30 (0.50–1085.70) 29.50 (0.00–8953.10)\nMean ± SD 48.14 ± 118.09 391.93 ± 1305.99 0.068\nCEA (ng/mL)\nMedian (range) 1.50 (0.60–5.20) 2.20 (0.70–30.00)\nMean ± SD 1.75 ± 0.99 4.05 ± 5.25 <0.001\nHE4 (pmol/L)\nMedian (range) 42.30 (28.10–107.70) 72.7 (28.7–1873.70)\nMean ± SD 45.19 ± 12.26 215.52 ± 336.46 <0.001\nCA125 (U/mL)\nMedian (range) 58.25 (10.10–5525.20) 147.20 (1.00–9426.00)\nMean ± SD 159.70 ± 575.42 691.53 ± 1402.11 0.013\nHb (g/mL)\nMedian (range) 12.60 (8.90–14.60) 12.80 (4.60–15.70)\nMean ± SD 12.60 ± 1.06 12.46 ± 1.88 0.691\nD-dimer (µg/mL)\nMedian (range) 0.70 (0.50–8.40) 1.30 (0.40–34.70)\nMean ± SD 0.99 ± 1.11 3.05 ± 4.87 <0.001\nOE ovarian endometrioma, EAOC endometriosis-associated ovarian cancer, CA 19-9 carbohydrate antigen\n19-9, CEA carcinoembryonic antigen, HE4 human epididymis protein 4, CA125 carbohydrate antigen125, Hb\nhemoglobin.\n3.2. The Characteristics of Each Biological Marker in Each Cohort\nThe results of the ROC curve analysis based on the detection of malignant tumors\nare shown in Figure 1, concerning each predictive index, and in Figures 2 and 3 regarding\nother biological markers. The optimal cutoff value was determined by analyzing the\nROC curve among malignant ovarian tumors and OE. Table 3 shows the cut-off values\ndiscriminating benign from malignant tumors for each cohort. In the post-menopause\ncohort, CEA and tumor diameter, which comprise the R2 predictive index, did not reach\nsignificant differentiation; on the other hand, CA125, comprising the CPH index and the\nROMA index, in the pre-menopause cohort did not reach significant differentiation. This\ncharacteristic influences the AUC of each index, including the CPH index, the ROMA index,\nand the R2 predictive index.\n\nDiagnostics 2022, 12, 1212 6 of 16\nDiagnostics 2022, 12, x FOR PEER REVIEW 6 of 15 \n \n \n \nFigure 1. The ROC curves of each predictive index in the combined cohort. The row indicates each \npredictive index and the column indicates each cohort. The R2 predictive index showed a high AUC \nin the pre-menopausal cohort; on the contrary, the ROMA and CPH indexes showed high AUCs in \npost-menopausal cohort. \n \nFigure 2. The ROC curves of other factors. The row indicates each factor, and the column indicates \neach cohort. \nFigure 1. The ROC curves of each predictive index in the combined cohort. The row indicates each\npredictive index and the column indicates each cohort. The R2 predictive index showed a high AUC\nin the pre-menopausal cohort; on the contrary, the ROMA and CPH indexes showed high AUCs in\npost-menopausal cohort.\nDiagnostics 2022, 12, x FOR PEER REVIEW 6 of 15 \n \n \n \nFigure 1. The ROC curves of each predictive index in the combined cohort. The row indicates each \npredictive index and the column indicates each cohort. The R2 predictive index showed a high AUC \nin the pre-menopausal cohort; on the contrary, the ROMA and CPH indexes showed high AUCs in \npost-menopausal cohort. \n \nFigure 2. The ROC curves of other factors. The row indicates each factor, and the column indicates \neach cohort. \nFigure 2. The ROC curves of other factors. The row indicates each factor, and the column indicates\neach cohort.\n\nDiagnostics 2022, 12, 1212 7 of 16\nDiagnostics 2022, 12, x FOR PEER REVIEW 7 of 15 \n \n \n \nFigure 3. The ROC curves of each tumor marker. CEA showed a higher AUC than HE4 and CA125 \nin the pre-menopausal cohort; however, in the post-menopausal cohort HE4 and CA125 increased \ntheir AUC in the post-menopausal cohort. \nTable 3. The cut-off values discriminating EAOC from benign OE in the pre-, post-menopausal, and \ncombined cohorts. \n AUC p-Value Cut-Off \nValue Sensitivity Specificity PPV NPV \nCA 19-9 (U/mL)        \nPre-menopause 0.511 0.872 – – – – – \nPost-menopause 0.765 0.062 – – – – – \nCombined 0.581 0.068 – – – – – \nCEA (ng/mL)        \nPre-menopause 0.704 0.002 1.55 0.750 0.615 33.96 90.32 \nPost-menopause 0.465 0.796 – – – – – \nCombined 0.714 <0.001 1.65 0.707 0.635 60.22 73.49 \nHE4 (pmol/L)        \nPre-menopause 0.631 0.049 82.90 0.375 0.989 90.00 85.71 \nPost-menopause 0.878 0.006 54.10 0.725 1.000 100.00 26.31 \nCombined 0.758 <0.001 54.65 0.627 0.854 77.04 74.54 \nCA125 (U/mL)        \nPre-menopause 0.606 0.112 – – – – – \nPost-menopause 0.898 0.004 15.00 0.922 0.800 97.91 50.00 \nCombined 0.610 0.013 146.15 0.507 0.844 71.69 68.64 \nTumor diameter (mm)        \nPre-menopause 0.772 <0.001 97.50 0.542 0.923 65.00 88.42 \nPost-menopause 0.758 0.059 – – – – – \nCombined 0.726 <0.001 97.50 0.541 0.927 85.10 71.77 \nBMI        \nPre-menopause 0.636 0.041 21.94 0.500 0.780 37.50 85.54 \nPost-menopause 0.718 0.111 – – – – – \nCombined 0.636 0.002 21.94 0.520 0.750 61.90 66.66 \nFigure 3. The ROC curves of each tumor marker. CEA showed a higher AUC than HE4 and CA125\nin the pre-menopausal cohort; however, in the post-menopausal cohort HE4 and CA125 increased\ntheir AUC in the post-menopausal cohort.\nTable 3. The cut-off values discriminating EAOC from benign OE in the pre-, post-menopausal, and\ncombined cohorts.\nAUC p-Value Cut-Off\nValue Sensitivity Specificity PPV NPV\nCA 19-9 (U/mL)\nPre-menopause 0.511 0.872 – – – – –\nPost-menopause 0.765 0.062 – – – – –\nCombined 0.581 0.068 – – – – –\nCEA (ng/mL)\nPre-menopause 0.704 0.002 1.55 0.750 0.615 33.96 90.32\nPost-menopause 0.465 0.796 – – – – –\nCombined 0.714 <0.001 1.65 0.707 0.635 60.22 73.49\nHE4 (pmol/L)\nPre-menopause 0.631 0.049 82.90 0.375 0.989 90.00 85.71\nPost-menopause 0.878 0.006 54.10 0.725 1.000 100.00 26.31\nCombined 0.758 <0.001 54.65 0.627 0.854 77.04 74.54\n\nDiagnostics 2022, 12, 1212 8 of 16\nTable 3. Cont.\nAUC p-Value Cut-Off\nValue Sensitivity Specificity PPV NPV\nCA125 (U/mL)\nPre-menopause 0.606 0.112 – – – – –\nPost-menopause 0.898 0.004 15.00 0.922 0.800 97.91 50.00\nCombined 0.610 0.013 146.15 0.507 0.844 71.69 68.64\nTumor diameter\n(mm)\nPre-menopause 0.772 <0.001 97.50 0.542 0.923 65.00 88.42\nPost-menopause 0.758 0.059 – – – – –\nCombined 0.726 <0.001 97.50 0.541 0.927 85.10 71.77\nBMI\nPre-menopause 0.636 0.041 21.94 0.500 0.780 37.50 85.54\nPost-menopause 0.718 0.111 – – – – –\nCombined 0.636 0.002 21.94 0.520 0.750 61.90 66.66\nD-dimer (µg/mL)\nPre-menopause 0.675 0.013 0.65 0.870 0.453 32.78 92.59\nPost-menopause 0.848 0.011 0.95 0.720 1.000 100.00 26.31\nCombined 0.748 <0.001 1.15 0.562 0.884 75.00 71.30\nCPH-I (%)\nPre-menopause 0.642 0.032 6.564 0.500 0.923 63.15 87.50\nPost-menopause 0.918 0.002 1.884 0.863 1.000 100.00 41.66\nCombined 0.758 <0.001 6.564 0.613 0.927 86.79 75.42\nROMA Index (%)\nPre-menopause 0.633 0.046 24.78 0.375 0.989 90.00 85.71\nPost-menopause 0.918 0.002 13.23 0.882 1.000 100.00 45.45\nCombined – – – – – 98.18 81.89\nR2 Predictive Index\nPre-menopause 0.840 <0.001 16.95 0.934 0.750 75.00 93.40\nPost-menopause 0.684 0.177 18.39 1.000 0.627 100.00 20.83\nCombined 0.777 <0.001 16.95 0.938 0.640 88.88 76.92\nCA 19-9 carbohydrate antigen 19-9, CEA carcinoembryonic antigen, HE4 human epididymis protein 4, CA125\ncarbohydrate antigen125, BMI body mass index, CPH-I Copenhagen index, ROMA risk of ovarian malignancy\nalgorithm, PPV positive predictive value, NPV negative predictive value, AUC area under curve.\n3.3. The Usefulness of Each Index in Discriminating OE and Malignant Ovarian T umors\nIn the combined cohort, some factors indicating malignant ovarian tumors (i.e., EAOC)\nwere extracted using a univariate analysis (Table 4). A multivariate analysis confirmed\nthat the ROMA index, the R2 predictive index, and tumor laterality were extracted as\nindependent factors for predicting malignant tumors (HR: 222.14, 95% confidence interval\n(CI): 22.27–2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90–33.13, p < 0.001; HR: 0.15, 95% CI:\n0.03–0.75, p = 0.021, respectively). Furthermore, excluding the CPH index, the ROMA\nindex, and the R2 predictive index, a multivariate analysis showed that laterality, tumor\ndiameter, D-dimer, CEA, and HE4 were the independent factors (HR: 0.22, 95% CI:0.08–0.65,\np = 0.006; HR: 12.68, 95% CI: 4.21–38.22, p < 0.001; HR: 5.13, 95% CI: 1.81–14.53, p = 0.002;\nHR: 4.36, 95% CI: 1.75–10.85, p = 0.002; HR: 3.85, 95% CI: 1.37–10.82, p = 0.011, respectively)\n\nDiagnostics 2022, 12, 1212 9 of 16\n(Table 4). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH\nindex and the R2 predictive index were extracted as independent factors for predicting\nmalignant tumors (HR: 6.45, 95% CI: 1.47–28.22, p = 0.013; HR: 31.19, 95% CI: 8.48–114.74,\np < 0.001, respectively). Excluding the CPH index, the ROMA index, and the R2 predictive\nindex, a multivariate analysis showed that laterality, tumor diameter, and HE4 were the\nindependent factors (HR: 0.15, 95% CI: 0.02–0.81, p = 0.028; HR: 11.78, 95% CI: 3.09–44.93,\np < 0.001; HR: 47.94, 95% CI: 4.01–572.03, p = 0.002, respectively) (Table 5). In the combined\ncohort, the ROMA index showed the highest diagnostic accuracy (Table 6) and a similar\nresult as the univariate analysis (Table 4). However, in the pre-menopausal cohort, the\nROMA index showed the highest accuracy (Table 6), but this did not remain using a\nunivariate analysis (Table 5).\nTable 4. Univariate and multivariable analysis of the predictive factors of EAOC in the\ncombined cohort.\nUnivariate Analysis Multivariate Analysis\nRisk Ratio\n(95% CI) p-Value Risk Ratio\n(95% CI) p-Value Risk Ratio\n(95% CI) p-Value\nCPH-I ≤6.564 1.00 (referent) — —\n(%) >6.564 20.16 (8.20–49.54) <0.001 — —\nROMA\nIndex 1.00 (referent) 1.00 (referent) — —\n(%) 244.28\n(31.96–1866.91) <0.001 222.14\n(22.27–2215.50) <0.001 — —\nR2\nPredictive ≤16.95 1.00 (referent) 1.00 (referent) — —\nIndex >16.95 26.66\n(10.29–69.05) <0.001 9.80 (2.90–33.13) <0.001 — —\nGravida 0 1.00 (referent)\n≥1 2.68 (1.43–5.02) 0.002\nParity 0 1.00 (referent)\n≥1 3.00 (1.60–5.63) 0.001\nLaterality Uni- 1.00 (referent) 1.00 (referent) 1.00 (referent)\nBi- 0.32 (0.16–0.66) 0.002 0.15 (0.03–0.75) 0.021 0.22 (0.08–0.65) 0.006\nBMI ≤21.94 1.00 (referent)\n>21.94 3.25 (1.70–6.20) <0.001\nTumor\ndiameter <97.50 1.00 (referent) — — 1.00 (referent)\n(mm) ≥97.50 14.53 (5.94–35.49) <0.001 — — 12.68 (4.21–38.22) <0.001\nD-dimer <1.15 1.00 (referent) 1.00 (referent)\n(µg/mL) ≥1.15 7.45 (3.60–15.42) <0.001 5.13 (1.81–14.53) 0.002\nCEA <1.65 1.00 (referent) — — 1.00 (referent)\n(ng/mL) ≥1.65 4.19 (2.19–8.02) <0.001 — — 4.36 (1.75–10.85) 0.002\nHE4 <54.65 1.00 (referent) — — 1.00 (referent)\n(pmol/L) ≥54.65 9.83 (4.71–20.50) <0.001 — — 3.85 (1.37–10.82) 0.011\nCA125 <146.15 1.00 (referent) — —\n(U/mL) ≥146.15 5.54 (2.71–11.31) <0.001 — —\nCPH-I Copenhagen index, ROMA risk of ovarian malignancy algorithm, BMI body mass index, CEA carcinoem-\nbryonic antigen, HE4 human epididymis protein 4, CA125 carbohydrate antigen125.\n\nDiagnostics 2022, 12, 1212 10 of 16\nTable 5. Univariate and Multivariable analysis of the predictive factors of EAOC in the pre-\nmenopausal cohort.\nUnivariate Analysis Multivariate Analysis\nRisk Ratio\n(95% CI) p-Value Risk Ratio\n(95% CI) p-Value Risk Ratio\n(95% CI) p-Value\nCPH-I ≤6.564 1.00 (referent) 1.00 (referent) — —\n(%) >6.564 12.00 (3.95–36.45) <0.001 6.45 (1.47–28.22) 0.013 — —\nROMA\nIndex ≤24.78 1.00 (referent) — —\n(%) >24.78 54.00\n(6.37–457.62) <0.001 — —\nR2\nPredictive ≤16.95 1.00 (referent) 1.00 (referent) — —\nIndex >16.95 42.50\n(12.29–146.95) <0.001 31.19\n(8.48–114.74) <0.001 — —\nGravida 0 1.00 (referent)\n≥1 1.04 (0.41–2.59) 0.929\nParity 0 1.00 (referent)\n≥1 1.25 (0.50–3.14) 0.627\nLaterality Uni- 1.00 (referent) 1.00 (referent)\nBi- 0.19 (0.05–0.71) 0.013 0.15 (0.02–0.81) 0.028\nBMI ≤21.94 1.00 (referent)\n>21.94 3.55 (1.38–9.10) 0.008\nTumor\ndiameter <97.50 1.00 (referent) — — 1.00 (referent)\n(mm) ≥97.50 14.18 (4.65–43.17) <0.001 — — 11.78 (3.09–44.93) <0.001\nD-dimer <0.65 1.00 (referent)\n(µg/mL) ≥0.65 6.09 (1.93–19.26) 0.002\nCEA <1.55 1.00 (referent) — —\n(ng/mL) ≥1.55 4.80 (1.73–13.25) 0.002 — —\nHE4 <82.90 1.00 (referent) — — 1.00 (referent)\n(pmol/L) ≥82.90 54.00\n(6.37–457.42) <0.001 — — 47.94\n(4.01–572.03) 0.002\nCPH-I Copenhagen index, ROMA risk of ovarian malignancy algorithm, BMI body mass index, CEA carcinoem-\nbryonic antigen, HE4 human epididymis protein 4.\nTable 6. Accuracy analysis among the three indexes.\nIndex Cohort PLR NLR DOR\nCPH Index Pre-menopause 6.50 0.54 12.00\nCombined 8.41 0.41 20.16\nROMA Index Pre-menopause 34.12 0.63 54.00\nCombined 69.12 0.28 244.28\nR2 Predictive\nIndex\nPre-menopause 11.37 0.26 42.50\nCombined 10.24 0.38 26.66\nPLR positive likelihood ratio, NLR negative likelihood ratio, DOR diagnostic odds ratio, CPH-I Copenhagen\nindex, ROMA risk of ovarian malignancy algorithm.\n3.4. The Usefulness of the R2 Predictive Index in Discriminating OE from Borderline T umors\nIn the combined cohort, some factors indicating a borderline tumor were extracted\nby the univariate analysis (Table 7). Multivariate analysis confirmed that the R2 predic-\ntive index was only extracted as an independent factor for predicting malignant tumors\n\nDiagnostics 2022, 12, 1212 11 of 16\n(HR: 45.00, 95% CI: 7.43–272.52, p < 0.001). When excluding the CPH index, the ROMA\nindex, and the R2 predictive index from the factor and including tumor diameter, CEA,\nHE4, and CA125, only tumor diameter was indicated as an independent factor (HR: 7.33,\n95% CI: 1.32–40.48, p = 0.022) (Table 7).\nTable 7. Univariate and multivariable analysis of the discriminating factors of borderline tumor from\nOE in the combined cohort.\nUnivariate Analysis Multivariate Analysis\nRisk Ratio\n(95% CI) p-Value Risk Ratio\n(95% CI) p-Value Risk Ratio\n(95% CI) p-Value\nCPH-I ≤6.564 1.00 (referent) — —\n(%) >6.564 4.23 (0.71–25.02) 0.111 — —\nROMA\nIndex 1.00 (referent) — —\n(%) 57.00\n(4.99–650.89) 0.001 — —\nR2\nPredictive ≤16.95 1.00 (referent) 1.00 (referent) — —\nIndex >16.95 45.00\n(7.43–272.52) <0.001 45.00\n(7.43–272.52) <0.001 — —\nGravida 0 1.00 (referent)\n≥1 2.23 (0.50–9.89) 0.289\nParity 0 1.00 (referent)\n≥1 2.65 (0.59–11.78) 0.198\nLaterality Uni- 1.00 (referent)\nBi- 0.46 (0.09–2.43) 0.366\nBMI ≤21.94 1.00 (referent)\n>21.94 5.00 (1.11–22.50) 0.036\nTumor\ndiameter <97.50 1.00 (referent) — — 1.00 (referent)\n(mm) ≥97.50 7.62 (1.50–38.74) 0.014 — — 7.33 (1.32–40.48) 0.022\nD-dimer <1.15 1.00 (referent)\n(µg/mL) ≥1.15 3.51 (0.75–16.38) 0.110\nCEA <1.65 1.00 (referent) — —\n(ng/mL) ≥1.65 5.22 (1.00–27.31) 0.050 — —\nHE4 <54.65 1.00 (referent) — —\n(pmol/L) ≥54.65 1.95 (0.35–10.66) 0.440 — —\nCA125 <146.15 1.00 (referent) — —\n(U/mL) ≥146.15 3.24 (0.69–15.01) 0.133 — —\nCPH-I Copenhagen index, ROMA risk of ovarian malignancy algorithm, BMI body mass index, CEA carcinoem-\nbryonic antigen, HE4 human epididymis protein 4, CA125 carbohydrate antigen125.\n3.5. The Differentiation of R2 Predictive Value between OE and Borderline T umor or Advanced\nMalignant T umors\nIn the combined cohort, the R2 predictive index, the ROMA index, and the CPH\nindex showed significant differentiation among ovarian endometriosis, borderline tumor,\nand carcinoma (Figure 4). The ROMA index and the CPH index could discriminate the\ncarcinoma from the others; on the contrary, the R2 predictive index discriminated the\nendometriosis from malignant tumors (Figure 4, Table 8).\n\nDiagnostics 2022, 12, 1212 12 of 16\nDiagnostics 2022, 12, x FOR PEER REVIEW 11 of 15 \n \n \nD-dimer <1.15 1.00 (referent)      \n(µg/mL) ≥1.15 3.51 (0.75–16.38) 0.110     \nCEA <1.65 1.00 (referent)  — —   \n(ng/mL) ≥1.65 5.22 (1.00–27.31) 0.050 — —   \nHE4 <54.65 1.00 (referent)  — —   \n(pmol/L) ≥54.65 1.95 (0.35–10.66) 0.440 — —   \nCA125 <146.15 1.00 (referent)  — —   \n(U/mL) ≥146.15 3.24 (0.69–15.01) 0.133 — —   \nCPH-I Copenhagen index, ROMA risk of ovarian malignancy algorithm, BMI body mass index, \nCEA carcinoembryonic antigen, HE4 human epididymis protein 4, CA125 carbohydrate antigen125. \n3.5. The Differentiation of R2 Predictive Value between OE and Borderline Tumor or Advanced \nMalignant Tumors \nIn the combined cohort, the R2 predictive index, the ROMA index, and the CPH in-\ndex showed significant differentiation among ovarian endometriosis, borderline tumor, \nand carcinoma (Figure 4). The ROMA index and the CPH index could discriminate the \ncarcinoma from the others; on the contrary, the R2 predictive index discriminated the en-\ndometriosis from malignant tumors (Figure 4, Table 8). \n \nFigure 4. To discriminate borderline tumors from ovarian endometriosis, the R2 predictive index \ncould be the most effective tool. ** p < 0.01 vs. others., *** p < 0.001 vs. others. The circles represent \noutliers. There were only two borderline cases in post-menopausal cohort (lower right). \nTable 8. The validation of R2 predictive index among tumor phenotypes. \n OE Borderline Tumor Carcinoma p-Value \nNumber n = 96 n = 8 n = 67  \nR2 Predictive Index     \nMedian (range) 19.80 (11.47–23.32) 13.27 (−20.60–20.43) 15.16 (−12.74–25.56)  \nMean ± SD 19.61 ± 2.13 6.81 ± 16.30 14.15 ± 7.00 0.001 \nOE ovarian endometrioma. \n4. Discussion \nIn the current study, the ROMA index, the CPH index, and the R2 predictive index \nwere shown to be effective tools to discriminate benign OE from EAOC, and showed sim-\nilar results to those reported previously [46,47]. In particular, in the combined cohort, the \nROMA index was the most effective predictor among  the three indexes (Table 4) ; how-\never, in the pre -menopausal cohort, the R2 predictive index was more effective than the \nothers (Table 5) for discriminating malignant tumors. This is partly because HE4 an d \nFigure 4. To discriminate borderline tumors from ovarian endometriosis, the R2 predictive index\ncould be the most effective tool. ** p < 0.01 vs. others., *** p < 0.001 vs. others. The circles represent\noutliers. There were only two borderline cases in post-menopausal cohort (lower right).\nTable 8. The validation of R2 predictive index among tumor phenotypes.\nOE Borderline T umor Carcinoma p-Value\nNumber n = 96 n = 8 n = 67\nR2 Predictive Index\nMedian (range) 19.80\n(11.47–23.32) 13.27 (−20.60–20.43) 15.16\n(−12.74–25.56)\nMean ± SD 19.61 ± 2.13 6.81 ± 16.30 14.15 ± 7.00 0.001\nOE ovarian endometrioma.\n4. Discussion\nIn the current study, the ROMA index, the CPH index, and the R2 predictive index\nwere shown to be effective tools to discriminate benign OE from EAOC, and showed\nsimilar results to those reported previously [46,47]. In particular, in the combined cohort,\nthe ROMA index was the most effective predictor among the three indexes (Table 4);\nhowever, in the pre-menopausal cohort, the R2 predictive index was more effective than\nthe others (Table 5) for discriminating malignant tumors. This is partly because HE4\nand CA125, which consist of the CPH and the ROMA index have a weaker ability to\ndiscriminate malignancy in pre-menopausal durations; on the other hand, CEA, which\nconsists of the R2 predictive value, was stronger in the pre-menopausal cohort than HE4\nand CA125 (Table 3). Serum CA125 levels are frequently measured when ovarian cysts are\nobserved, in order to rule out a malignant tumor. However, it is well known that elevated\nserum CA125 levels are not only seen in endometrioma [48], but also in adenomyosis [49]\nor menstrual cycle [50], thus giving a high rate of false positives [51,52]. This was confirmed\nin a recent Cochrane review, which reported that among the 97 biomarkers studied, CA125\nwas the only marker that is elevated in cases of endometrioma, with 40% sensitivity and\n91% specificity, with a cut-off limit of 35 U/mL [53].\nOn the other hand, HE4 is the most promising. HE4 protein is encoded by the WAP\nfour-disulfide core domain 2 (WFDC2) [54], which was found to be highly expressed in\novarian carcinoma, especially in serous and endometrioid cancers [55,56]. Unlike CA125,\n\nDiagnostics 2022, 12, 1212 13 of 16\nHE4 is not overexpressed in benign ovarian disease, normal ovarian tissue, or tumors\nwith low malignant potential [55]. Terlikowska KM et al. reported that the HE4 level in\nserum elevates with age, and the specificity was better in post-menopausal patients than in\npre-menopausal patients [57]. This trend is similar to that in our results, in which the CPH\nand the ROMA index were useful tools to discriminate malignancy in post-menopausal\npatients. In particular, in pre-menopausal patients there is a major challenge in choosing\nthe surgical method (i.e., laparotomy or laparoscopic surgery), and this index could be\nhelpful for the physician.\nWe previously reported that OE has a higher iron concentration than EAOC and\ncan discriminate either cyst fluid iron concentration or transverse magnetic relaxation\nrate R2 or R2* value, using a complex, chemical shift-encoded MR examination [ 37,38].\nHowever, no evidence concerning the standpoint of borderline tumor (i.e., the degree of\niron concentration or R2 value) exists, because of the rare incidence of this disease. We\ndemonstrated that the R2 predictive index was the independent factor to discriminate\nborderline tumor from OE in the combined cohort (Table 7). Moreover, the R2 predictive\nindex of OE was higher than other malignant tumors with significant differentiation\n(Table 8). We can hypothesize that borderline tumors could show lower iron concentrations\nthan OE, and this may discriminate benign OE from EAOC, even in borderline cases, by\niron concentration and transverse magnetic relaxation rate R2 or R2* value.\nThis study has some limitations. The first limitation is that the number of OE in\npost-menopausal patients was too small to assess the effectiveness of these indexes in\nthe post-menopausal cohort. Second, the sample sizes of the borderline ovarian tumor\nand phenotype were too small to conclude the efficacy of the R2 predictive index in\ndiscriminating borderline tumors from endometriosis, and further case accumulation is\nneeded.\n5. Conclusions\nIn conclusion, in pre-menopausal cases or borderline cases, the R2 predictive index is\nuseful; and in post-menopausal cases, the ROMA index is better than the other indexes.\nAuthor Contributions: Conceptualization, N.K.; methodology, N.K. and S.Y.; validation, N.K. and\nS.Y.; formal analysis, N.K.; investigation, N.K. and F.K.; resources, N.K., R.K., K.W., T.M., Y.F., R.M.\nand Y.Y.; data curation, N.K., S.Y. and F.K.; writing—original draft preparation, N.K.; writing—review\nand editing, S.Y., N.K., R.K., H.K. and F.K.; visualization, N.K.; supervision, H.K. and F.K.; project\nadministration, F.K.; funding acquisition, N.K.; All authors have read and agreed to the published\nversion of the manuscript.\nFunding: This research was funded by Japan Society for the Promotion of Science, grant number\n21K16819.\nInstitutional Review Board Statement: The study was conducted according to the guidelines of\nthe Declaration of Helsinki, and approved by the Institutional Ethics Committee of Nara Medical\nUniversity Hospital (protocol code: 2944 and 3115).\nInformed Consent Statement: The consent form making patients’ data available for research use\nwas obtained at the first hospitalization, and after approval by the Ethics Review Committee of the\nNara Medical Hospital, and the opt-out form was provided through our institutional homepage.\nData Availability Statement: The data presented in this study are available on request from the\ncorresponding author.\nAcknowledgments: The authors gives thanks for the measurement of tumor markers to Tosoh\nCorporation, Tokyo, Japan.\nConflicts of Interest: The authors declare no conflict of interest.\n\nDiagnostics 2022, 12, 1212 14 of 16\nReferences\n1. 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