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Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study.
Xiao, Mei Ling; Qian, Ting; Fu, Le; Wei, Yan; Ma, Feng Hua; Gu, Wei Yong; Li, Hai Ming; Li, Yong Ai; Qian, Zhao Xia; Cheng, Jie Jun; Zhang, Guo Fu; Qiang, Jin Wei.
Affiliation
  • Xiao ML; Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
  • Qian T; Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Fu L; Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
  • Wei Y; Department of Automation, Zhejiang University of Technology, Hangzhou, China.
  • Ma FH; Department of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China.
  • Gu WY; Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China.
  • Li HM; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Li YA; Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
  • Qian ZX; Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Cheng JJ; Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
  • Zhang GF; Department of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China.
  • Qiang JW; Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
J Magn Reson Imaging ; 59(4): 1394-1406, 2024 Apr.
Article in En | MEDLINE | ID: mdl-37392060
ABSTRACT

BACKGROUND:

Deep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate optimal therapy decision.

PURPOSE:

To develop a nomogram to identify DSI in cervical AC/ASC. STUDY TYPE Retrospective. POPULATION Six hundred and fifty patients (mean age of 48.2 years) were collected from center 1 (primary cohort, 536), centers 2 and 3 (external validation cohorts 1 and 2, 62 and 52). FIELD STRENGTH/SEQUENCE 5-T, T2-weighted imaging (T2WI, SE/FSE), diffusion-weighted imaging (DWI, EPI), and contrast-enhanced T1-weighted imaging (CE-T1WI, VIBE/LAVA). ASSESSMENT The DSI was defined as the outer 1/3 stromal invasion on pathology. The region of interest (ROI) contained the tumor and 3 mm peritumoral area. The ROIs of T2WI, DWI, and CE-T1WI were separately imported into Resnet18 to calculate the DL scores (TDS, DDS, and CDS). The clinical characteristics were retrieved from medical records or MRI data assessment. The clinical model and nomogram were constructed by integrating clinical independent risk factors only and further combining DL scores based on primary cohort and were validated in two external validation cohorts. STATISTICAL TESTS Student's t-test, Mann-Whitney U test, or Chi-squared test were used to compare differences in continuous or categorical variables between DSI-positive and DSI-negative groups. DeLong test was used to compare AU-ROC values of DL scores, clinical model, and nomogram.

RESULTS:

The nomogram integrating menopause, disruption of cervical stromal ring (DCSRMR), DDS, and TDS achieved AU-ROCs of 0.933, 0.807, and 0.817 in evaluating DSI in primary and external validation cohorts. The nomogram had superior diagnostic ability to clinical model and DL scores in primary cohort (all P < 0.0125 [0.05/4]) and CDS (P = 0.009) in external validation cohort 2. DATA

CONCLUSION:

The nomogram achieved good performance for evaluating DSI in cervical AC/ASC. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY Stage 2.
Subject(s)
Key words

Full text: 1 Database: MEDLINE Main subject: Adenocarcinoma / Uterine Cervical Neoplasms / Carcinoma, Adenosquamous / Deep Learning Type of study: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Middle aged Language: En Year: 2024 Type: Article

Full text: 1 Database: MEDLINE Main subject: Adenocarcinoma / Uterine Cervical Neoplasms / Carcinoma, Adenosquamous / Deep Learning Type of study: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Middle aged Language: En Year: 2024 Type: Article