Polycystic liver: automatic segmentation using deep learning on CT is faster and as accurate compared to manual segmentation

Sex, Genotype, and Liver Volume Progression as Risk of Hospitalization Determinants in Autosomal Dominant Polycystic Liver Disease

BACKGROUND & AIMS

Autosomal dominant polycystic liver disease is a rare condition with a female preponderance, based mainly on pathogenic variants in 2 genes, PRKCSH and SEC63. Clinically, autosomal dominant polycystic liver disease is characterized by vast heterogeneity, ranging from asymptomatic to highly symptomatic hepatomegaly. To date, little is known about the prediction of disease progression at early stages, hindering clinical management, genetic counseling, and the design of randomized controlled trials. To improve disease prognostication, we built a consortium of European and US centers to recruit the largest cohort of patients with PRKCSH and SEC63 liver disease.

METHODS

We analyzed an international multicenter cohort of 265 patients with autosomal dominant polycystic liver disease harboring pathogenic variants in PRKCSH or SEC63 for genotype-phenotype correlations, including normalized age-adjusted total liver volumes and polycystic liver disease-related hospitalization (liver event) as primary clinical end points.

RESULTS

Classifying individual total liver volumes into predefined progression groups yielded predictive risk discrimination for future liver events independent of sex and underlying genetic defects. In addition, disease severity, defined by age at first liver event, was considerably more pronounced in female patients and patients with PRKCSH variants than in those with SEC63 variants. A newly developed sex-gene score was effective in distinguishing mild, moderate, and severe disease, in addition to imaging-based prognostication.

CONCLUSIONS

Both imaging and clinical genetic scoring have the potential to inform patients about the risk of developing symptomatic disease throughout their lives. The combination of female sex, germline PRKCSH alteration, and rapid total liver volume progression is associated with the greatest odds of polycystic liver disease-related hospitalization.

Higher need for polycystic liver disease therapy in female patients: Sex-specific association between liver volume and need for therapy

BACKGROUND AND AIMS

Prognostic tools or biomarkers are urgently needed in polycystic liver disease (PLD) to monitor disease progression and evaluate treatment outcomes. Total liver volume (TLV) is currently used to assess cross-sectional disease severity, and female patients typically have larger livers than males. Therefore, this study explores the sex-specific association between TLV and volume-reducing therapy (VRT).

APPROACH AND RESULTS

In this prospective cohort study, we included patients with PLD from European treatment centers. We explored sex-specific differences in the association between baseline TLV and initiation of volume-reducing therapy and determined the cumulative incidence rates of volume-reducing therapy in our cohort.We included 358 patients, of whom 157 (43.9%) received treatment. Treated patients had a higher baseline TLV (median TLV 2.16 vs. 4.34 liter, p < 0.001), were more frequently female (69.7% vs. 89.8%, p < 0.001), and had a higher risk of liver events (HR 4.381, p < 0.001). The cumulative volume-reducing therapy rate at 1 year of follow-up was 21.0% for females compared to 9.1% for males. Baseline TLV was associated with volume-reducing therapy, and there was an interaction with sex (HR females 1.202, p < 0.001; HR males 1.790, p < 0.001; at 1.5 l).

CONCLUSION

Baseline TLV is strongly associated with volume-reducing therapy initiation at follow-up in patients with PLD, with sex-specific differences in this association. Disease staging systems should use TLV to predict the need for future volume-reducing therapy in PLD separately for males and females.

The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations

Fully supervised medical image segmentation methods use pixel-level labels to achieve good results, but obtaining such large-scale, high-quality labels is cumbersome and time consuming. This study aimed to develop a weakly supervised model that only used image-level labels to achieve automatic segmentation of four types of uterine lesions and three types of normal tissues on magnetic resonance images. The MRI data of the patients were retrospectively collected from the database of our institution, and the T2-weighted sequence images were selected and only image-level annotations were made. The proposed two-stage model can be divided into four sequential parts: the pixel correlation module, the class re-activation map module, the inter-pixel relation network module, and the Deeplab v3 + module. The dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD) were employed to evaluate the performance of the model. The original dataset consisted of 85,730 images from 316 patients with four different types of lesions (i.e., endometrial cancer, uterine leiomyoma, endometrial polyps, and atypical hyperplasia of endometrium). A total number of 196, 57, and 63 patients were randomly selected for model training, validation, and testing. After being trained from scratch, the proposed model showed a good segmentation performance with an average DSC of 83.5%, HD of 29.3 mm, and ASSD of 8.83 mm, respectively. As far as the weakly supervised methods using only image-level labels are concerned, the performance of the proposed model is equivalent to the state-of-the-art weakly supervised methods.

Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine

Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.

Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography - high speed and accuracy

OBJECTIVES

Polycystic liver disease (PLD) is characterized by growth of hepatic cysts, causing hepatomegaly. Disease severity is determined using total liver volume (TLV), which can be measured from computed tomography (CT). The gold standard is manual segmentation which is time-consuming and requires expert knowledge of the anatomy. This study aims to validate the commercially available semi-automatic MMWP (Multimodality Workplace) Volume tool for CT scans of PLD patients.

METHODS

We included adult patients with one (n = 60) or two (n = 46) abdominal CT scans. Semi-automatic contouring was compared with manual segmentation, using comparison of observed volumes (cross-sectional) and growth (longitudinal), correlation coefficients (CC), and Bland-Altman analyses with bias and precision, defined as the mean difference and SD from this difference. Inter- and intra-reader variability were assessed using coefficients of variation (CV) and we assessed the time to perform both procedures.

RESULTS

Median TLV was 5292.2 mL (IQR 3141.4-7862.2 mL) at baseline. Cross-sectional analysis showed high correlation and low bias and precision between both methods (CC 0.998, bias 1.62%, precision 2.75%). Absolute volumes were slightly higher for semi-automatic segmentation (manual 5292.2 (3141.4-7862.2) versus semi-automatic 5432.8 (3071.9-7960.2) mL, difference 2.7%, p < 0.001). Longitudinal analysis demonstrated that semi-automatic segmentation accurately measures liver growth (CC 0.908, bias 0.23%, precision 4.04%). Inter- and intra-reader variability were small (2.19% and 0.66%) and comparable to manual segmentation (1.21% and 0.63%) (p = 0.26 and p = 0.37). Semi-automatic segmentation was faster than manual tracing (19 min versus 50 min, p = 0.009).

CONCLUSIONS

Semi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients.

KEY POINTS

• Semi-automatic liver segmentation using the commercially available MMWP volume tool accurately determines total liver volume as well as liver growth over time in polycystic liver disease patients. • This method is considerably faster than manual segmentation through the use of Hounsfield unit settings. • We used a real-life CT set for the validation and showed that the semi-automatic tool measures accurately regardless of contrast used for the CT scan or not, presence of polycystic kidneys, liver volume, and previous invasive treatment for polycystic liver disease.