A CT-based radiomics nomogram for differentiating ovarian cystadenomas and endometriotic cysts

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor

STUDY OBJECTIVE

To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis.

DESIGN

A retrospective cohort study.

SETTING

This study was conducted at a tertiary referral center.

PATIENTS

A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio.

INTERVENTIONS

Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation.

MEASUREMENTS AND MAIN RESULTS

In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor.

CONCLUSION

This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography

Endometriosis affects 1 in 9 women, taking 6.4 years to diagnose using conventional laparoscopy. Non-invasive imaging enables timelier diagnosis, reducing diagnostic delay, risk and expense of surgery. This review updates literature exploring the diagnostic value of specialist endometriosis magnetic resonance imaging (eMRI), nuclear medicine (NM) and computed tomography (CT). Searching after the 2016 IDEA consensus, 6192 publications were identified, with 27 studies focused on imaging for endometriosis. eMRI was the subject of 14 papers, NM and CT, 11, and artificial intelligence (AI) utilizing eMRI, 2. eMRI papers describe diagnostic accuracy for endometriosis, methodologies, and innovations. Advantages of eMRI include its: ability to diagnose endometriosis in those unable to tolerate transvaginal endometriosis ultrasound (eTVUS); a panoramic pelvic view, easy translation to surgical fields; identification of hyperintense iron in endometriotic lesions; and ability to identify super-pelvic lesions. Sequence standardization means eMRI is less operator-dependent than eTVUS, but higher costs limit its role to a secondary diagnostic modality. eMRI for deep and ovarian endometriosis has sensitivities of 91-93.5% and specificities of 86-87.5% making it reliable for surgical mapping and diagnosis. Superficial lesions too small for detection in larger capture sequences, means a negative eMRI doesn't exclude endometriosis. Combined with thin sequence capture and improved reader expertise, eMRI is poised for rapid adoption into clinical practice. NM labeling is diagnostically limited in absence of suitable unique marker for endometrial-like tissue. CT studies expose the reproductively aged to radiation. AI diagnostic tools, combining independent eMRI and eTVUS endometriosis markers, may result in powerful capability. Broader eMRI use, will optimize standards and protocols. Reporting systems correlating to surgical anatomy will facilitate interdisciplinary preoperative dialogues. eMRI endometriosis diagnosis should reduce repeat surgeries with mental and physical health benefits for patients. There is potential for early eMRI diagnoses to prevent chronic pain syndromes and protect fertility outcomes.

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

BACKGROUND

This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors.

METHODS

The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram.

RESULTS

Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0.

CONCLUSIONS

The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.