Clinical features and survival outcomes between ascending and descending types of nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data intelligence platform-based analysis

Yao, Ji‐Jin; Qi, Zhenyu; Liu, Zhigang; Jiang, Guanmin; Xu, Xiwei; Chen, Shaoyi; Zhu, Fengting; Zhang, Wangjian; Lawrence, Wayne R.; Ma, Jingfei; Zhou, Guan-Qun

doi:10.1016/j.radonc.2019.04.025

Cited by 30 publications

(45 citation statements)

References 29 publications

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“…Previous literatures reported approximately 90% local control rates for NPC in the IMRT era [27,28]. Due to recent advances in RT technology, no observed associated differences in LRRFS between patients in high RWL and low RWL groups were found in the present study.…”

Section: Discussioncontrasting

confidence: 43%

Dynamic variation and prognostic value of weight loss during radiotherapy in nasopharyngeal carcinoma: A large-scale cohort study

Jin

Xia

Mai

et al. 2020

Preprint

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Background: We investigated the dynamic variation and prognostic value of weight loss among nasopharyngeal carcinoma (NPC) patients. Methods: 1149 newly diagnosed NPC patients receiving radical radiotherapy (RT) were retrospectively analyzed. Patients’ weights were measured at initiation of RT and every week during RT. Recursive partitioning analyses (RPAs) were utilized to determine cut-off value for rate of weight loss (RWL). To assess predictive value of RWL survival analysis was performed. Disease-free survival (DFS) was our primary endpoint. Secondary endpoints included locoregional relapse-free survival (LRRFS), distant metastasis-free survival (DMFS), and overall survival (OS).Results: RWLs were 0%, 0%, -1.54%, -2.86%, -4.11%, -5.98%, -6.56% at 1st, 2nd, 3rd, 4th, 5th, 6th, and 7th week of RT, respectively. RWL optimal threshold with respect to DFS was 5.3% based on RPAs. Thus, a consistent threshold of -5% (>-5% versus ≤-5%) was selected to classify NPC patients into low RWL and high RWL groups for survival analysis. Compared to high RWL (≤-5%), patients with low RWL (>-5%) had significantly better ten-year DFS (78.6% versus 61.2%; P< 0.001), OS (86.6% versus 70.1%; P< 0.001), and DMFS (88.5% versus 80.2%; P = 0.007). However, no difference was observed between LRRFS groups (91.7% versus 94.3%; P= 0.173). In multivariate analysis, RWL ≤-5% was an independent risk factor for DFS (HR, 1.56; 95% CI, 1.19-2.03; P= 0.001), OS (HR, 1.54; 95% CI, 1.11-2.15; P= 0.011) and DMFS (HR, 1.47; 95% CI, 1.03-2.10; P= 0.033) in patients with NPC. Additionally, chemotherapy and old age were attributed to the development of high RWL.Conclusions: Weight loss during RT was significantly associated with decreased survival among NPC patients. Clinicians should continuously inform patients on the health impact of minimizing weight loss under 5%.

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Section: Discussioncontrasting

confidence: 43%

Dynamic variation and prognostic value of weight loss during radiotherapy in nasopharyngeal carcinoma: A large-scale cohort study

Jin

Xia

Mai

et al. 2020

Preprint

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“…Our study results were consistent with the findings of studies conducted in numerous endemic areas. 7,8 Type A tumors were more likely to recur, and type D tumors were more likely to metastasize. In endemic areas, patients with type D tumors have a worse 5-year OS than patients with type A tumors.…”

Section: Discussionmentioning

confidence: 99%

Clinical characteristics and survival outcomes of ascending, descending and mixed types of nasopharyngeal carcinoma in the non‐endemic areas of china: A propensity score matching analysis

et al. 2020

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“…We expect tumors to respond after receiving the treatment, however, it is not uncommon for the "best treatment plan" to yield poor results. Radiotherapy resistance, recurrence, distant metastasis, and complications caused by radiotherapy are analyzed and considered in many studies as the main causes of treatment failure and patient death (37)(38)(39)(40). Oncologists could adjust the predetermined treatment plan according to the obtained evidence to match the estimated tumor risk.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Locally Advanced Nasopharyngeal Carcinoma Prognostication Based on Pre- and Post-treatment MRI

Deng

Hua

et al. 2020

Preprint

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Background: Although it has been reported by several studies that using AI to predict the prognosis of nasopharyngeal carcinoma (NPC) based on magnetic resonance (MR) image, the information around the tumor was not valued and the post-treatment MR images were ignored. Herein we aimed to predict the prognosis of advanced NPC (stage Ⅲ-Ⅳa) using pre- and post-treatment MR images based on deep learning (DL).Methods: A total of 206 patients with primary NPC who were diagnosed and treated at the Renmin Hospital of Wuhan University between June 2012 and January 2018 were retrospectively selected. A rectangular region of interest (ROI), which included the tumor area, surrounding tissues and organs, was delineated on each pre- and post-treatment MR image. Two InceptionResnetV2-based transfer learning models, named pre-model and post-model, were trained with the Pre-dataset and the Post-dataset, respectively. In addition, an ensemble learning model based on the pre-model and post-models was trained. The three established models were evaluated by receiver operating characteristic (ROC) analysis, confusion matrix, and Harrell’s concordance indices (C-index) after the model test. High-risk-related heat maps were developed according to the DL models.Results: The pre-model, post-model, and ensemble models displayed a C-index of 0.717 (95% CI: 0.639 to 0.795), 0.811 (95% CI: 0.745–0.877), 0.830 (95% CI: 0.767–0.893), and AUC of 0.745 (95% CI: 0.592–0.897), 0.820 (95% CI: 0.687–0.953), and 0.841 (95% CI: 0.715–0.968) for the test cohort, respectively. In comparison with the models, the post-model performance was better than the pre-model, which indicated the importance of post-treatment images for prognosis prediction. All three DL models performed better than the TNM staging system. The captured features presented on heat maps showed that the areas around the tumor and lymph nodes were related to the prognosis of the tumor.Conclusions: The three established DL models based on pre- and post-treatment MR images have a better performance than TNM staging. Post-treatment MR images are of great significance for prognosis prediction and could contribute to clinical decision-making.

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Clinical features and survival outcomes between ascending and descending types of nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data intelligence platform-based analysis

Cited by 30 publications

References 29 publications

Dynamic variation and prognostic value of weight loss during radiotherapy in nasopharyngeal carcinoma: A large-scale cohort study

Dynamic variation and prognostic value of weight loss during radiotherapy in nasopharyngeal carcinoma: A large-scale cohort study

Clinical characteristics and survival outcomes of ascending, descending and mixed types of nasopharyngeal carcinoma in the non‐endemic areas of china: A propensity score matching analysis

Deep Learning for Locally Advanced Nasopharyngeal Carcinoma Prognostication Based on Pre- and Post-treatment MRI

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