Haiming Li

A modified diffusion-weighted magnetic resonance imaging–based model from the radiologist’s perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer

Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Diffusion‐Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High‐Grade Serous Ovarian Carcinoma

BackgroundPreoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high‐grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded.PurposeTo investigate the value of diffusion‐weighted MRI (DW‐MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients.Study TypeProspective.SubjectsA total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years).Fields Strength/SequenceA 3.0 T, readout‐segmented echo‐planar DWI.AssessmentThe MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI‐PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI‐PCI alone; and a fusion model combining the clinical‐morphological information and DWI‐PCI.Statistical TestsMultivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P &lt; 0.05 was considered to be statistically significant.ResultsSixty‐seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI‐PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA‐125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI‐PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI.Data ConclusionsThe combination of two clinical factors, MRI morphological characteristics and DWI‐PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS.Evidence Level2Technical EfficacyStage 2

Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early‐Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study

BackgroundDeep 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.PurposeTo develop a nomogram to identify DSI in cervical AC/ASC.Study TypeRetrospective.PopulationSix 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/Sequence5‐T, T2‐weighted imaging (T2WI, SE/FSE), diffusion‐weighted imaging (DWI, EPI), and contrast‐enhanced T1‐weighted imaging (CE‐T1WI, VIBE/LAVA).AssessmentThe 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 TestsStudent'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.ResultsThe 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 &lt; 0.0125 [0.05/4]) and CDS (P = 0.009) in external validation cohort 2.Data ConclusionThe nomogram achieved good performance for evaluating DSI in cervical AC/ASC.Level of Evidence3Technical EfficacyStage 2