Shin-Hyung Park

Mapping patterns of para-aortic lymph node recurrence in cervical cancer: a retrospective cohort analysis

Abstract Background To map anatomic patterns of para-aortic lymph node (PALN) recurrence in cervical cancer patients and validate currently available guidelines on PA clinical target volumes (CTV). Methods Cervical cancer patients who developed PALN recurrence were included. The PALNs were classified as left-lateral para-aortic (LPA), aorto-caval (AC), and right para-caval (RPC). Four PA CTVs were contoured for each patient to validate PALN coverage. CTV RTOG was contoured based on the Radiation Therapy Oncology Group guideline. CTV K was contoured as proposed by Keenan et al. CTV M was contoured by expanding symmetrical margins around the aorta and inferior vena cava of 7 mm up to the T12–L1 interspace. CTV new was created by modifying CTV RTOG to obtain better coverage. Results We identified 92 PALNs in 35 cervical cancer patients. 46.8% of the PALNs were at LPA, 38.0% were at AC, and 15.2% were at RPC areas. CTV RTOG , CTV K , and CTV M covered 87.0%, 88.0%, and 62.0% of all PALNs, respectively. PALN recurrence above the left renal vein was associated with PALN involvement at diagnosis ( p  = 0.043). Extending upper border to the superior mesenteric artery allowed the CTV new to cover 96.7% of all PALNs and all nodes in 91.4% of patients. Conclusion CTV RTOG and CTV K encompassed most PALN recurrences. For high-risk patients, such as those having PALN involvement at diagnosis, extending the superior border of CTV from the left renal vein to superior mesenteric artery could be considered.

Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis

Abstract Background Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients. Methods We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31). Results For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset. Conclusions Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.

Robustness of magnetic resonance radiomic features to pixel size resampling and interpolation in patients with cervical cancer

Abstract Background Radiomics is a promising field in oncology imaging. However, the implementation of radiomics clinically has been limited because its robustness remains unclear. Previous CT and PET studies suggested that radiomic features were sensitive to variations in pixel size and slice thickness of the images. The purpose of this study was to assess robustness of magnetic resonance (MR) radiomic features to pixel size resampling and interpolation in patients with cervical cancer. Methods This retrospective study included 254 patients with a pathological diagnosis of cervical cancer stages IB to IVA who received definitive chemoradiation at our institution between January 2006 and June 2020. Pretreatment MR scans were analyzed. Each region of cervical cancer was segmented on the axial gadolinium-enhanced T1- and T2-weighted images; 107 radiomic features were extracted. MR scans were interpolated and resampled using various slice thicknesses and pixel spaces. Intraclass correlation coefficients (ICCs) were calculated between the original images and images that underwent pixel size resampling (OP), interpolation (OI), or pixel size resampling and interpolation (OP+I) as well as among processed image sets with various pixel spaces (P), various slice thicknesses (I), and both (P + I). Results After feature standardization, ≥86.0% of features showed good robustness when compared between the original and processed images (OP, OI, and OP+I) and ≥ 88.8% of features showed good robustness when processed images were compared (P, I, and P + I). Although most first-order, shape, and texture features showed good robustness, GLSZM small-area emphasis-related features and NGTDM strength were sensitive to variations in pixel size and slice thickness. Conclusion Most MR radiomic features in patients with cervical cancer were robust after pixel size resampling and interpolation following the feature standardization process. The understanding regarding the robustness of individual features after pixel size resampling and interpolation could help future radiomics research.

Oncologic outcomes according to the level of disease burden in patients with metachronous distant metastases from uterine cervical cancer: a Korean Radiation Oncology Group study (KROG 18-10)

This study aimed to evaluate the oncologic outcomes according to disease burden in uterine cervical cancer patients with metachronous distant metastases. Between 2005 and 2015, 163 patients with metachronous distant metastases from uterine cervical cancer after receiving a definitive therapy were evaluated at seven institutions in Korea. Low metastatic burden was defined as less than 5 metastatic sites, whereas high metastatic burden was others. Each metastasis site was divided based on the lymph node (LN) and organs affected. The overall survival (OS) and progression-free survival (PFS) were assessed. Cox proportional hazards models, including other clinical variables, were used to evaluate the survival outcomes. The median follow-up duration was 22.2 months (range: 0.3-174.8 months). Para-aortic LNs (56.4%), lungs (26.4%), supraclavicular LNs (18.4%), and peritoneum (13.5%) were found to be the common metastasis sites. Among 37 patients with a single metastasis, 17 (45.9%) had LN metastases and 20 (54.1%) had organ metastases. The 1- and 2-year OS rates were 73.9% and 55.0%, respectively, whereas the PFS rates were 67.2% and 42.9%, respectively. SCC Ag after recurrence and high metastatic burden were significant factors affecting the OS (p=0.004 and p<0.001, respectively). Distant organ recurrence, short disease-free interval (≤2 years), and high metastatic burden were unfavorable factors for PFS (p=0.003, p=0.011, and p=0.002, respectively). A favorable oncologic outcome can be expected by performing salvage treatments in selected patients with a long disease-free interval, low metastatic burden, and/or lymphatic-only metastasis.