The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Vandetanib induces a marked anti-tumor effect and amelioration of ectopic Cushing’s syndrome in a medullary thyroid carcinoma patient

A 55-year-old woman diagnosed with sporadic MTC underwent total thyroidectomy 20 years ago. After the first surgery, elevated calcitonin levels in parallel with local disease persistence were noted and therefore she underwent repeated neck dissections. During follow-up, multiple foci of metastatic disease were noted in the neck and mediastinal lymph nodes, lungs and bones; however, the disease had an indolent course for a number of years, in parallel with a calcitonin doubling time of more than two years and without significant symptoms. During a routine follow-up visit 2 years ago, findings suggestive of Cushing’s syndrome were observed on physical examination. The biochemical evaluation demonstrated markedly elevated serum calcitonin level, in parallel with lack of cortisol suppression after an overnight 1 mg dexamethasone suppression test, lack of cortisol and ACTH suppression after high-dose IV dexamethasone 8 mg, elevated plasma ACTH up to 79 pg/mL (normal <46 pg/mL) and elevated 24-h urinary free cortisol up to 501 μg/24 h (normal 9–90 μg/24 h). After a negative pituitary MRI, she underwent IPSS, which was compatible with EAS. Whole-body CT demonstrated progressive disease at most of the tumor sites. Treatment with vandetanib at a dosage of 200 mg/day was commenced. The patient showed a significant, rapid and consistent clinical improvement already after two months of treatment, in parallel with biochemical improvement, whereas a decrease in tumor size was demonstrated on follow-up CT

Differentiation of Heterogeneous Mouse Liver from HCC by Hyperpolarized ¹³C Magnetic Resonance

The clinical characterization of small hepatocellular carcinoma (HCC) lesions in the liver and differentiation from heterogeneous inflammatory or fibrotic background is important for early detection and treatment. Metabolic monitoring of hyperpolarized 13C-labeled substrates has been suggested as a new avenue for diagnostic magnetic resonance. The metabolism of hyperpolarized [1-13C]pyruvate was monitored in mouse precision-cut liver slices (PCLS) of aged MDR2-KO mice, which served as a model for heterogeneous liver and HCC that develops similarly to the human disease. The relative in-cell activities of lactate dehydrogenase (LDH) to alanine transaminase (ALT) were found to be 0.40 ± 0.06 (n = 3) in healthy livers (from healthy mice), 0.90 ± 0.27 (n = 3) in heterogeneously inflamed liver, and 1.84 ± 0.46 (n = 3) in HCC. Thus, the in-cell LDH/ALT activities ratio was found to correlate with the progression of the disease. The results suggest that the LDH/ALT activities ratio may be useful in the assessment of liver disease. Because the technology used here is translational to both small liver samples that may be obtained from image-guided biopsy (i.e., ex vivo investigation) and to the intact liver (i.e., in a noninvasive MRI scan), these results may provide a path for differentiating heterogeneous liver from HCC in human subjects

In-Cell Determination of Lactate Dehydrogenase Activity in a Luminal Breast Cancer Model – <i>ex vivo</i> Investigation of Excised Xenograft Tumor Slices Using dDNP Hyperpolarized [1-¹³C]pyruvate

[1-13C]pyruvate, the most widely used compound in dissolution-dynamic nuclear polarization (dDNP) magnetic resonance (MR), enables the visualization of lactate dehydrogenase (LDH) activity. This activity had been demonstrated in a wide variety of cancer models, ranging from cultured cells, to xenograft models, to human tumors in situ. Here we quantified the LDH activity in precision cut tumor slices (PCTS) of breast cancer xenografts. The Michigan Cancer Foundation-7 (MCF7) cell-line was chosen as a model for the luminal breast cancer type which is hormone responsive and is highly prevalent. The LDH activity, which was manifested as [1-13C]lactate production in the tumor slices, ranged between 3.8 and 6.1 nmole/nmole adenosine tri-phosphate (ATP) in 1 min (average 4.6 &#177; 1.0) on three different experimental set-ups consisting of arrested vs. continuous perfusion and non-selective and selective RF pulsation schemes and combinations thereof. This rate was converted to an expected LDH activity in a mass ranging between 3.3 and 5.2 &#181;mole/g in 1 min, using the ATP level of these tumors. This indicated the likely utility of this approach in clinical dDNP of the human breast and may be useful as guidance for treatment response assessment in a large number of tumor types and therapies ex vivo

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.ISSN:1361-8415ISSN:1361-842