Guang Yang

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi‐Parametric MRI by Deep Learning

BackgroundEarly and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi‐parametric MRI (mpMRI) images.PurposeTo develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.Study TypeRetrospective.PopulationSix hundred twenty‐one patients with histologically proven EC from two institutions, including 111 LNM‐positive and 168 LVSI‐positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively.Field Strength/SequenceT2‐weighted imaging (T2WI), contrast‐enhanced T1WI (CE‐T1WI), and diffusion‐weighted imaging (DWI) were scanned with turbo spin‐echo, gradient‐echo, and two‐dimensional echo‐planar sequences, using either a 1.5 T or 3 T system.AssessmentEC lesions were manually delineated on T2WI by two radiologists and used to train an nnU‐Net model for automatic segmentation. A multi‐task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE‐T1WI, and DWI images as inputs. The performance of the model for LNM‐positive diagnosis was compared with those of three radiologists in the external test cohort.Statistical TestsDice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant.ResultsEC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674).Data ConclusionThe proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.Evidence Level3Technical EfficacyStage 2

Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging

To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images. Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models were trained with 397 patients and validated with 170 internal and 31 external patients. For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively. Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi‐spectral photoacoustic tomography

AbstractIn photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO2). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi‐pixel method to smooth the measurements before calculating rHbT and %sO2. We first perform phantom studies using blood tubes of calibrated %sO2 to evaluate the accuracy of our %sO2 estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co‐registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO2 and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90‐0.96) on the testing data set was achieved by combining mean %sO2, a spectral feature, and the score of the study radiologist.