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PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma.
Song, Jiangdian; Zhang, Jie; Liu, Guichao; Guo, Zhexu; Liao, Hongxian; Feng, Wenhui; Lin, Wenxiang; Li, Lei; Zhang, Yi; Yang, Yuxiang; Liu, Bin; Luo, Ruibang; Chen, Hao; Wang, Siyun; Liu, Jian-Hua.
Afiliación
  • Song J; School of Health Management, China Medical University, Shenyang, China. song.jd0910@gmail.com.
  • Zhang J; Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
  • Liu G; The Fifth Affiliated Hospital of Sun Yat-Sen University, Guangdong, China.
  • Guo Z; Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors China Medical University, Ministry of Education, Shenyang, China.
  • Liao H; Department of VIP In-Patient Ward, The First Hospital of China Medical University, Shenyang, China.
  • Feng W; Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
  • Lin W; Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
  • Li L; Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
  • Zhang Y; Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
  • Yang Y; Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China.
  • Liu B; Department of Radiology, The Second People's Hospital of Xiangzhou, Zhuhai, China.
  • Luo R; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
  • Chen H; Department of Computer Science, The University of Hong Kong, Hong Kong, Hong Kong.
  • Wang S; Department of Gastroenterology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China. gd_chenhao1981@126.com.
  • Liu JH; Department of Oncology, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. wangsiyun@gdph.org.cn.
Insights Imaging ; 15(1): 161, 2024 Jun 24.
Article en En | MEDLINE | ID: mdl-38913225
ABSTRACT

OBJECTIVES:

The clinical decision-making regarding choosing surgery alone (SA) or surgery followed by postoperative adjuvant chemotherapy (SPOCT) in esophageal squamous cell carcinoma (ESCC) remains controversial. We aim to propose a pre-therapy PET/CT image-based deep learning approach to improve the survival benefit and clinical management of ESCC patients.

METHODS:

This retrospective multicenter study included 837 ESCC patients from three institutions. Prognostic biomarkers integrating six networks were developed to build an ESCC prognosis (ESCCPro) model and predict the survival probability of ESCC patients treated with SA and SPOCT. Patients who did not undergo surgical resection were in a control group. Overall survival (OS) was the primary end-point event. The expected improvement in survival prognosis with the application of ESCCPro to assign treatment protocols was estimated by comparing the survival of patients in each subgroup. Seven clinicians with varying experience evaluated how ESCCPro performed in assisting clinical decision-making.

RESULTS:

In this retrospective multicenter study, patients receiving SA had a median OS 9.2 months longer than controls. No significant differences in survival were found between SA patients with predicted poor outcomes and the controls (p > 0.05). It was estimated that if ESCCPro was used to determine SA and SPOCT eligibility, the median OS in the ESCCPro-recommended SA group and SPOCT group would have been 15.3 months and 24.9 months longer, respectively. In addition, ESCCPro also significantly improved prognosis accuracy, certainty, and the efficiency of clinical experts.

CONCLUSION:

ESCCPro assistance improved the survival benefit of ESCC patients and the clinical decision-making among the two treatment approaches. CRITICAL RELEVANCE STATEMENT The ESCCPro model for treatment decision-making is promising to improve overall survival in ESCC patients undergoing surgical resection and patients undergoing surgery followed by postoperative adjuvant chemotherapy. KEY POINTS ESCC is associated with a poor prognosis and unclear ideal treatments. ESCCPro predicts the survival of patients with ESCC and the expected benefit from SA. ESCCPro improves clinicians' stratification of patients' prognoses.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Insights Imaging Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Insights Imaging Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania