liver lesion
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2022 ◽  
pp. 028418512110701
Jonas Oppenheimer ◽  
Keno Kyrill Bressem ◽  
Fabian Henry Jürgen Elsholtz ◽  
Bernd Hamm ◽  
Stefan Markus Niehues

Background Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability. Purpose To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection. Material and Methods Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion. Results A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRSTMILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRSTMILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRSTSTR showing the closest results ( P < 0.001). Conclusion Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

2021 ◽  
Jacob Johnson ◽  
Kaneel Senevirathne ◽  
Lawrence Ngo

In this work, we report the results of a deep-learning based liver lesion detection algorithm. While several liver lesion segmentation and classification algorithms have been developed, none of the previous work has focused on detecting suspicious liver lesions. Furthermore, their generalizability remains a pitfall due to their small sample size and sample homogeneity. Here, we developed and validated a highly generalizable deep-learning algorithm for detection of suspicious liver lesions. The algorithm was trained and tested on a diverse dataset containing CT exams from over 2,000 hospital sites in the United States. Our final model achieved an AUROC of 0.84 with a specificity of 0.99 while maintaining a sensitivity of 0.33.

2021 ◽  
Vol 11 (1) ◽  
Narine Mesropyan ◽  
Petra Mürtz ◽  
Alois M. Sprinkart ◽  
Wolfgang Block ◽  
Julian A. Luetkens ◽  

AbstractThis study investigated the impact of different ROI placement and analysis methods on the diagnostic performance of simplified IVIM-DWI for differentiating liver lesions. 1.5/3.0-T DWI data from a respiratory-gated MRI sequence (b = 0, 50, 250, 800 s/mm2) were analyzed in patients with malignant (n = 74/54) and benign (n = 35/19) lesions. Apparent diffusion coefficient ADC = ADC(0,800) and IVIM parameters D1′ = ADC(50,800), D2′ = ADC(250,800), f1′ = f(0,50,800), f2′ = f(0,250,800), and D*' = D*(0,50,250,800) were calculated voxel-wise. For each lesion, a representative 2D-ROI, a 3D-ROI whole lesion, and a 3D-ROI from “good” slices were placed, including and excluding centrally deviating areas (CDA) if present, and analyzed with various histogram metrics. The diagnostic performance of 2D- and 3D-ROIs was not significantly different; e.g. AUC (ADC/D1′/f1′) were 0.958/0.902/0.622 for 2D- and 0.942/0.892/0.712 for whole lesion 3D-ROIs excluding CDA at 1.5 T (p > 0.05). For 2D- and 3D-ROIs, AUC (ADC/D1′/D2′) were significantly higher, when CDA were excluded. With CDA included, AUC (ADC/D1′/D2′/f1′/D*') improved when low percentiles were used instead of averages, and was then comparable to the results of average ROI analysis excluding CDA. For lesion differentiation the use of a representative 2D-ROI is sufficient. CDA should be excluded from ROIs by hand or automatically using low percentiles of diffusion coefficients.

Golo Petzold ◽  
Philipp Ströbel ◽  
Ali Seif Amir Hosseini ◽  
Volker Ellenrieder ◽  
Albrecht Neesse

AbstractCystic liver lesions (CLL) are common and, in the majority of cases, benign. However, the range of differential diagnoses of CLL is wide. A combination of medical history, blood test results, and imaging can help find the correct diagnosis. We report the case of a 38-year-old immunocompromised female patient with a history of thymectomy and postoperative radiation 3 years prior due to thymoma. Subsequently, the patient was referred to our department for clarification of a cystic liver lesion. During short-term follow-up, the lesion increased in size, and due to the contrast agent behavior in the ultrasound and MRI examination, the suspicion of a biliary cystadenocarcinoma was considered.Furthermore, imaging showed several subcentimetric liver lesions of unknown dignity. Finally, pericystectomy and atypical partial liver resection was performed. Histology revealed a cystic metastasis of the malignant B3 thymoma and a cavernous hemangioma. Liver metastases of a thymoma are rare, and this is the first case of a cystic liver metastasis of a thymoma. The presented case illustrates that in the management of CLLs beside imaging techniques, the medical history with previous conditions should be considered, especially in past malignancies.

2021 ◽  
pp. 1638-1644
Veena Gullapalli ◽  
Hannah Hsu ◽  
Vanita Bhargava ◽  
Peter Presgrave

Somatic malignant transformation of germ cell tumours is a well-described but poorly understood phenomenon. It is characterized by differentiation of pluripotent teratoma cells into somatic tumour cells. Following malignant transformation, the most common histologies are sarcomas and primitive neuroectodermal tumours; however, other subtypes have been recognized including melanoma, leukaemia, and renal cell carcinoma. We report a case of a 38-year-old male who had recently completed treatment for a mediastinal germ cell tumour with teratomatous components. He presented several months after completion of chemotherapy with metastatic lesions in his spine and liver accompanied with severe pancytopenia. He was subsequently diagnosed with acute megakaryoblastic leukaemia (AMKL), and a biopsy of a liver lesion was consistent with metastatic melanoma. This case illustrates the simultaneous development of 2 rare malignant entities: mediastinal germ cell tumour-associated AMKL and somatic malignant transformation to melanoma. It also highlights the importance of close surveillance to detect these metastatic sequelae and the emerging role of tumour sequencing to establish targetable pathways.

Davide Lanza ◽  
Mentor Bilali

Hepatic adenomatosis (HA) is a very rare condition and defined as the presence of 10 or more adenomas in an otherwise normal liver. HA has an incidence of 10–24% in patient with hepatic adenoma and it is more common in women. Most patients with HA are asymptomatic with a normal liver function test and half of cases are detected incidentally on imaging. Although HA is considered a benign disease, some patients may develop potentially fatal complications, such as hypovolaemic shock due to rupture of the liver lesion or malignant transformation to hepatocellular carcinoma. We report the case of a 29-year-old woman who presented to the emergency room after a car accident. Whole-body computed tomography revealed multiple focal hepatic hypervascular lesions in the right lobe of the liver together with a fatty liver. Subsequent hepatic magnetic resonance imaging suggested the diagnosis of HA with a suspicion of focal nodular hyperplasia (FNH). The patient refused to undergo liver biopsy, so we instituted a 3-month surveillance program, which included clinical assessment, liver function tests, tumour marker assessment and blood tests as well as sonographic evaluation for follow-up of the liver lesions.

2021 ◽  
pp. 106689692110498
Harumi Nakamura ◽  
Yuki Koyanagi ◽  
Masanori Kitamura ◽  
Yoji Kukita

Rhabdomyosarcoma (RMS) is a soft tissue tumor with striated muscle cell differentiation. It mostly occurs in children. While it can affect any part of the body, it commonly involves the urogenital organs, head and neck including the parameninges and orbit, and limbs. We describe an adult case of primary epithelioid RMS of the liver. A 71-year-old man presented with a 5.6 cm liver mass. Tumor histology revealed diffuse proliferation of small epithelioid cells and focal spindle cells. The tumor cells were immunohistochemically positive for myogenin (positive ratio 30%), desmin, Myo D1, and CD56. The tumor weakly expressed MDM2 and did not express CDK4. This suggested that dedifferentiated liposarcoma with a rhabdomyosarcomatous component was unlikely. There was no fusion gene of PAX3-FKHR or PAX7- FKHR to indicate alveolar RMS by RT-PCR. Subsequently, RNA Pan-Cancer Targeted sequencing was performed for 1385 genes revealed a single base substitution (c.742C>T) in TP53 that changes an amino acid (p.Arg248Trp). No fusion gene was found. No other primary RMS lesions were detected aside from the liver lesion. The tumor was diagnosed as a primary epithelioid RMS of the liver. His RMS already metastasized to the lymph nodes of the entire body. The patient declined further therapy and died one year later. This was the first case report of a primary epithelioid RMS of the liver.

2021 ◽  
Yanfen Shi ◽  
Dingrong Zhong ◽  
Yuanliang Li ◽  
Huangying Tan ◽  
Zhaoqing Li ◽  

Abstract Background: Pancreatic neuroendocrine neoplasms(p-NENs) are classified into neuroendocrine tumors (NET) G1, G2, G3, and neuroendocrine carcinoma (NEC) according to WHO classification. NET and NEC are different pathogenesis. The two kinds of tumors that occurred in the same part have not been reported. We found 4 foci of NEN G3 in a primary pancreatic NET G2. The cell atypia was obvious with Ki67 index of 50-70%, focal necrosis, there were 12 hepatic metastatic nodules with similar morphology to NEN G3, which is difficult to identify NEC and NET G3.Case presentation: A patient with pancreatic NET was selected to perform whole exome sequencing on primary pancreatic NET G2 and liver metastatic NEN G3 paraffin tissues.NET G2 had 13 somatic mutations, while NEN G3 had 72 somatic mutations and Copy number variation in 4 genes. P.S493N point mutation of TRIOBP gene was detected in NET G2 and NEN G3. 5-fold amplification of MDM4 is found in the metastatic liver lesion.Conclusion: NET G2 and NEN G3 are closely related to TRIOBP gene. Oncogene amplification (MDM4) in liver metastases may be associated with morphological malignant transformation.

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1599
Wen-Fan Chen ◽  
Hsin-You Ou ◽  
Cheng-Tang Pan ◽  
Chien-Chang Liao ◽  
Wen Huang ◽  

Due to the fact that previous studies have rarely investigated the recognition rate discrepancy and pathology data error when applied to different databases, the purpose of this study is to investigate the improvement of recognition rate via deep learning-based liver lesion segmentation with the incorporation of hospital data. The recognition model used in this study is H-DenseUNet, which is applied to the segmentation of the liver and lesions, and a mixture of 2D/3D Hybrid-DenseUNet is used to reduce the recognition time and system memory requirements. Differences in recognition results were determined by comparing the training files of the standard LiTS competition data set with the training set after mixing in an additional 30 patients. The average error value of 9.6% was obtained by comparing the data discrepancy between the actual pathology data and the pathology data after the analysis of the identified images imported from Kaohsiung Chang Gung Memorial Hospital. The average error rate of the recognition output after mixing the LiTS database with hospital data for training was 1%. In the recognition part, the Dice coefficient was 0.52 after training 50 epochs using the standard LiTS database, while the Dice coefficient was increased to 0.61 after adding 30 hospital data to the training. After importing 3D Slice and ITK-Snap software, a 3D image of the lesion and liver segmentation can be developed. It is hoped that this method could be used to stimulate more research in addition to the general public standard database in the future, as well as to study the applicability of hospital data and improve the generality of the database.

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