Zhikai Liu

Is Prophylactic Radiotherapy to the Lymphatic Drainage Area Necessary for Patients With Stage III Ovarian Cancer After Chemotherapy Following Surgery?

Background For patients with stage III epithelial ovarian cancer, there are limited studies on the effects of postoperative adjuvant radiotherapy (RT). Here we assessed the therapeutic efficacy and toxicity of postoperative radiotherapy to the abdominal and pelvic lymphatic drainage area for stage III epithelial ovarian cancer patients, who had all received surgery and chemotherapy (CT). Methods We retrospectively collected patients with stage III epithelial ovarian cancer after cytoreductive surgery (CRS) and full-course adjuvant CT. The chemoradiotherapy (CRT) group patients were treated with intensity modulated radiotherapy (IMRT) to the abdominal and pelvic lymphatic drainage area in our hospital between 2010 and 2020. A propensity score matching analysis was conducted to compare the results between the CRT and CT groups. Kaplan-Meier analysis estimated overall survival (OS), disease-free survival (DFS), and local control (LC) rates. The log-rank test determined the significance of prognostic factors. Results A total of 132 patients with median follow-up of 73.9 months (9.1-137.7 months) were included (44 and 88 for the CRT and RT groups, retrospectively). The baseline characteristics of age, histology, level of CA12-5, surgical staging, residual tumour, courses of adjuvant CT, and courses to reduce CA12-5 to normal were all balanced. The median DFS time, 5-year OS, and local recurrence free survival (LRFS) were 100.0 months vs 25.9 months ( P = .020), 69.2% vs 49.9% ( P = .002), and 85.9% vs 50.5% ( P = .020), respectively. The CRT group mainly presented with acute haematological toxicities, with no statistically significant difference compared with grade III intestinal adverse effects (3/44 vs 6/88, P = .480). Conclusion This report demonstrates that long-term DFS could be achieved in stage III epithelial ovarian cancer patients treated with IMRT preventive radiation to the abdominal and pelvic lymphatic area. Compared with the CT group, DFS and OS were significantly prolonged and adverse effects were acceptable.

Cervical Cancer in Pregnancy: A 10-Year Retrospective Analysis of Clinical Management and Future Perspectives

Introduction This study aims to evaluate diagnosis, treatment and clinical outcomes for patients with cervical cancer in pregnancy (CCIP) and their fetuses over a 10-year period, providing clinical evidence for the management of CCIP. Methods Clinical data of 28 patients diagnosed with CCIP at our center between January 1st, 2013 and June 30th, 2023 were retrospectively analyzed, focusing on gestational age at diagnosis, treatment, and maternal-fetal outcomes. Results A total of 28 patients with CCIP were identified, accounting for 0.42% (28/6678) of patients with cervical cancer during the study period. The majority of patients (86%, 24/28) had squamous cell carcinoma diagnosed by colposcopic biopsy, and 21 patients presented with recurrent vaginal bleeding. Cervical cancer was diagnosed during pregnancy in 19 cases and in the postpartum period in 9 cases. The mean tumor diameter was 5.4 (2-12) cm. Among 19 patients diagnosed during pregnancy, 13 patients chose pregnancy preservation, resulting in an average delay of treatment by 16.4 (0-33) weeks without observed disease progression. Fetuses were delivered via cesarean section at an average gestational age of 36.3 weeks; eight of these patients received neoadjuvant chemotherapy. At a median follow-up duration of 40.1 (12-103) months, 25 patients survived. Disease-free survival was observed in 20 patients, whereas two patients experienced local progression, and six developed distant metastases. Conclusion Clinical outcomes for patients with CCIP appear comparable to those observed in non-pregnant patients in the general population. Pregnant patients presenting with abnormal vaginal bleeding should undergo prompt cervical cancer screening to enable early diagnosis and tailored management strategies. For patients with a strong desire to maintain their pregnancy, careful consideration should be given to postponing delivery until fetal maturity, thereby minimizing maternal and fetal complications and improving maternal and fetal outcomes.

Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy

The delineation of the clinical target volume (CTV) is a crucial, laborious and subjective step in cervical cancer radiotherapy. The aim of this study was to propose and evaluate a novel end-to-end convolutional neural network (CNN) for fully automatic and accurate CTV in cervical cancer. A total of 237 computed tomography (CT) scans of patients with locally advanced cervical cancer were collected and evaluated. A novel 2.5D CNN network, called DpnUNet, was developed for CTV delineation and further applied for CTV and organ-at-risk (OAR) delineation simultaneously. Comprehensive comparisons and experiments were performed. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD) and subjective evaluation were used to assess the performance of this method. The mean DSC and 95HD values were 0.86 and 5.34 mm for the delineated CTVs. The clinical experts' subjective assessments showed that 90% of the predicted contours were acceptable for clinical usage. The mean DSC and 95HD values were 0.91 and 4.05 mm for bladder, 0.85 and 2.16 mm for bone marrow, 0.90 and 1.27 mm for left femoral head, 0.90 and 1.51 mm for right femoral head, 0.82 and 4.29 mm for rectum, 0.85 and 4.35 mm for bowel bag, 0.82 and 4.96 mm for spinal cord respectively. The average delineation time for one patient's CT images was within 15 seconds. The experimental results demonstrate that the CTV and OARs delineated for cervical cancer by DpnUNet was in close agreement with the ground truth. DpnUNet could significantly reduce the radiation oncologists' contouring time.

Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network

We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time. We collected 105 patients' Computed Tomography (CT) scans that diagnosed locally advanced cervical cancer and treated with radiotherapy in one hospital. Seven organs, including the bladder, bone marrow, left femoral head, right femoral head, rectum, small intestine and spinal cord were defined as OARs. The annotated contours of the OARs previously delineated manually by the patient's radiotherapy oncologist and confirmed by the professional committee consisted of eight experienced oncologists before the radiotherapy were used as the ground truth masks. A multi-class segmentation model based on U-Net was designed to fulfil the OARs segmentation task. The Dice Similarity Coefficient (DSC) and 95th Hausdorff Distance (HD) are used as quantitative evaluation metrics to evaluate the proposed method. The mean DSC values of the proposed method are 0.924, 0.854, 0.906, 0.900, 0.791, 0.833 and 0.827 for the bladder, bone marrow, femoral head left, femoral head right, rectum, small intestine, and spinal cord, respectively. The mean HD values are 5.098, 1.993, 1.390, 1.435, 5.949, 5.281 and 3.269 for the above OARs respectively. Our proposed method can help reduce the inter-observer and intra-observer variability of manual OARs delineation and lessen oncologists' efforts. The experimental results demonstrate that our model outperforms the benchmark U-Net model and the oncologists' evaluations show that the segmentation results are highly acceptable to be used in radiation therapy planning.