Background Cervix Squamous cell carcinoma(CSCC) is the most common pathological subtypes of cervix carcinoma(CC). CSCC can be divided into poorly differentiated, moderately differentiated and well-differentiated types. The pathological differentiation is essential for the treatment and prognosis of CSCC. Compared with the well-differentiated CSCC patients, poorly differentiated CSCC patients have poor clinical prognosis. The biopsy is the golden standard for identifying pathological differentiation with the disadvantages including invasive. Therefore, an imaging method is needed to determine the degree of tumor differentiation before surgery. Purpose The objective is to explore APTw and IVIM values in diagnosing the differentiation degree of cervical squamous cell carcinoma (CSCC). Methods APTw was scanned by using 3D Multi-shot TSE for obtaining APT signal intensity (APT SI). IVIM was scanned by using 12 b values (0, 20, 100, 150, 200, 300, 400, 500, 600, 800, 1000 and 1200 s/mm2) to calculate parameters: D, D*, and f. ADC was calculated based on 2 b values (0, 800 s/mm2). The parameters among different groups were compared by t-tests. Diagnostic performance was evaluated with a ROC analysis. Results 56 patients and 30 healthy volunteers were included in study. Patients were divided into: a well-moderately differentiated group (n = 34) and a poorly differentiated group (n = 22). The parameters (APT SI, ADC, D, f) were statistically significantly different between CSCC and normal cervix. APT SI of the CSCC was higher than that of normal cervix (P < 0.001). The ADC, D, and f of the CSCC were lower than those of normal cervix (P < 0.001). Significant differences were found in APT SI and D between the well-moderately differentiated and poorly differentiated group (P < 0.001). Comparing the well-moderately differentiated and poorly differentiated group, AUC of APT SI, D and f were 0.789, 0.775 ,and 0.670, sensitivity were 72.73%, 68.18%, 77.27%, and specificity were 79.41%, 82.35%, 64.71%, respectively (P < 0.05). Conclusion APTw and IVIM can be used to diagnose CSCC and provide accurate quantitative information. Compared with IVIM, APTw has higher diagnostic performance in identifying the differentiation degree of CSCC.

Objectives: Accurate preoperative diagnosis of small cell neuroendocrine cancer of the cervix (SCNECC) is crucial for establishing the best treatment plan. This study aimed to develop an improved, noninvasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information. Methods: A total of 105 pathologically confirmed cervical cancer patients (35 SCNECC, 70 non-SCNECC) from multiple centres with complete clinical and MR records were included. Whole lesion histogram analysis of the ADC was performed. Multivariate logistic regression analysis was used to develop diagnostic models based on clinical, morphological, and histogram data. The predictive performance in terms of discrimination, calibration, and clinical usefulness of the different models were assessed. A nomogram for preoperatively discriminating SCNECC was developed from the combined model. Results: In preoperative SCNECC diagnosis, the combined model, which had a diagnostic AUC (area under the curve) of 0.937 (95% CI: 0.887–0.987), outperformed the clinical-morphological model, which had an AUC of 0.869 (CI: 0.788–0.949), and the histogram model, which had an AUC of 0.872 (CI: 0.792–0.951). The calibration curve and decision curve analyses suggest that the combined model achieved good fitting and clinical utility. Conclusions: Noninvasive preoperative diagnosis of SCNECC can be achieved with high accuracy by integrating clinical, MR morphological, and ADC histogram features. The nomogram derived from the combined model can provide an easy-to-use clinical preoperative diagnostic tool for SCNECC. Advances in knowledge: It is clear that the therapeutic strategies for small cell neuroendocrine cancer of the cervix (SCNECC) are different from those for other pathological types of cervical cancer according to Version 1.2021) of the NCCN clinical practice guidelines in oncology for cervical cancer. This research developed an improved, noninvasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information.