Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (-766 vs. -790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62-0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules.

Incidental Pulmonary Nodules: Differential Diagnosis and Clinical Management.

BACKGROUND: According to data from the USA, the incidence of incidentally discovered pulmonary nodules is 5.8 per 1000 person-years for women and 5.2 per 1000 person-years for men. Their management as recommended in the pertinent guidelines can substantially improve clinical outcomes. More than 95% of all pulmonary nodules revealed by computed tomography (CT) are benign, but many cases are not managed in conformity with the guidelines. In this article, we summarize the appropriate clinical approach and provide an overview of the pertinent diagnostic studies and when they should be performed. METHODS: This review is based on relevant publications retrieved by a selective search in PubMed. The authors examined English-language recommendations issued since 2010 for the management of pulmonary nodules, supplemented by comments from the German lung cancer guideline. RESULTS: In general, the risk that an incidentally discovered pulmonary nodule is malignant is low but rises markedly with increasing size and the presence of risk factors. When such a nodule is detected, the further recommendation, depending on size, is either for follow-up examinations with chest CT or else for an extended evaluation with positron emission tomography-CT and biopsy for histology. The diagnostic evaluation should include consideration of any earlier imaging studies that may be available as an indication of possible growth over time. Single nodules measuring less than 6 mm, in patients with few or no risk factors, do not require any follow-up. Lung cancer is diagnosed in just under 10% of patients with a nodule measuring more than 8 mm. CONCLUSION: The recommendations of the guidelines for the management of incidentally discovered pulmonary nodules are intended to prevent both over- and undertreatment. If a tumor is suspected, further care should be provided by an interdisciplinary team.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.

OBJECTIVE: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. METHODS: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). RESULTS: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. CONCLUSION: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. CRITICAL RELEVANCE STATEMENT: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging. KEY POINTS: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Diagnostic Potential of Supplemental Static and Dynamic 68Ga-FAPI-46 PET for Primary 18F-FDG-Negative Pulmonary Lesions.

PET using 68Ga-labeled fibroblast activation protein (FAP) inhibitors (FAPIs) holds high potential for diagnostic imaging of various malignancies, including lung cancer (LC). However, 18F-FDG PET is still the clinical gold standard for LC imaging. Several subtypes of LC, especially lepidic LC, are frequently 18F-FDG PET-negative, which markedly hampers the assessment of single pulmonary lesions suggestive of LC. Here, we evaluated the diagnostic potential of static and dynamic 68Ga-FAPI-46 PET in the 18F-FDG-negative pulmonary lesions of 19 patients who underwent surgery or biopsy for histologic diagnosis after PET imaging. For target validation, FAP expression in lepidic LC was confirmed by FAP immunohistochemistry. Methods: Hematoxylin and eosin staining and FAP immunohistochemistry of 24 tissue sections of lepidic LC from the local tissue bank were performed and analyzed visually. Clinically, 19 patients underwent static and dynamic 68Ga-FAPI-46 PET in addition to 18F-FDG PET based on individual clinical indications. Static PET data of both examinations were analyzed by determining SUVmax, SUVmean, and tumor-to-background ratio (TBR) against the blood pool, as well as relative parameters (68Ga-FAPI-46 in relation to18F-FDG), of histologically confirmed LC and benign lesions. Time-activity curves and dynamic parameters (time to peak, slope, k 1, k 2, k 3, and k 4) were extracted from dynamic 68Ga-FAPI-46 PET data. The sensitivity and specificity of all parameters were analyzed by calculating receiver-operating-characteristic curves. Results: FAP immunohistochemistry confirmed the presence of strongly FAP-positive cancer-associated fibroblasts in lepidic LC. LC showed markedly elevated 68Ga-FAPI-46 uptake, higher TBRs, and higher 68Ga-FAPI-46-to-18F-FDG ratios for all parameters than did benign pulmonary lesions. Dynamic imaging analysis revealed differential time-activity curves for LC and benign pulmonary lesions: initially increasing time-activity curves with a decent slope were typical of LC, and steadily decreasing time-activity curve indicated benign pulmonary lesions, as was reflected by a significantly increased time to peak and significantly smaller absolute values of the slope for LC. Relative 68Ga-FAPI-46-to-18F-FDG ratios regarding SUVmax and TBR showed the highest sensitivity and specificity for the discrimination of LC from benign pulmonary lesions. Conclusion: 68Ga-FAPI-46 PET is a powerful new tool for the assessment of single 18F-FDG-negative pulmonary lesions and may optimize patient stratification in this clinical setting.

Characterization of three new SERPINA1 variants PiQ0Heidelberg II, PiQ0Heidelberg III and PiQ0Heidelberg IV in patients with severe alpha-1 antitrypsin deficiency.

Background: The clinical and molecular characteristics of three patients with previously unreported SERPINA1 mutations associated with severe alpha-1 antitrypsin deficiency (AATD) are described. The pathophysiology of the chronic obstructive pulmonary disease (COPD) present in these patients was characterized through clinical, biochemical, and genetic examinations. Case presentations: Case 1: A 73-year-old male with bilateral centri-to panlobular emphysema and multiple increasing ventrobasal bullae and incomplete fissures, COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade III B), progressive dyspnea on exertion (DOE), AAT level of 0.1-0.2 g/L. Genetic testing revealed a unique SERPINA1 mutation: Pi*Z/c.1072C > T. This allele was designated PiQ0Heidelberg II. Case 2: A 47-year-old male with severely heterogenous centri-to panlobular emphysema concentrated in the lower lobes, COPD GOLD IV D with progressive DOE, AAT <0.1 g/L. He also had a unique Pi*Z/c.10del mutation in SERPINA1. This allele was named PiQ0Heidelberg III. Case 3: A 58-year-old female with basally accentuated panlobular emphysema, GOLD II B COPD, progressive DOE. AAT 0.1 g/L. Genetic analysis revealed Pi*Z/c.-5+1G > A and c.-472G > A mutations in SERPINA1. This variant allele was named PiQ0Heidelberg IV. Conclusions: Each of these patients had a unique and previously unreported SERPINA1 mutation. In two cases, AATD and a history of smoking led to severe lung disease. In the third case, timely diagnosis, and institution of AAT replacement stabilized lung function. Wider screening of COPD patients for AATD could lead to faster diagnosis and earlier treatment of AATD patients with AATD which could slow or prevent progression of their disease.

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone. MATERIALS AND METHODS: In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation. RESULTS: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007). CONCLUSIONS: In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment.

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively. RESULTS: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets. CONCLUSIONS: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.

Clinical characterization of a novel alpha1-antitrypsin null variant: PiQ0Heidelberg.

The clinical characterization of a null variant of SERPINA1 - PiQ0Heidelberg - resulting in alpha1-antitrypsin (AAT) deficiency is described. This rare mutation (c.-5+5 G > A) has been previously identified but not clinically described. The 77 year-old female patient had GOLD-3, Group B COPD, severe destructive panlobular emphysema and newly observed respiratory failure on exertion at the time the genetic analysis was performed. Serum AAT level was 0.1 g/L (reference 0.9-2.0 g/L). Isoelectric focusing showed only the Z-protein indicating that this was a null mutation. The patient has started AAT replacement. Early screening and identification of AAT deficiency would allow for earlier intervention.

Augmentation therapy with human alpha-1-proteinase inhibitor reduces exacerbations in patient with bronchiectasis and alpha-1-antitrypsin deficiency.

Alpha-1-antitrypsin deficiency (AATD) is a rare cause of noncystic fibrosis (CF) bronchiectasis. The benefits of augmentation therapy in patients with chronic obstructive pulmonary disease (COPD) and pulmonary emphysema are well established. The role of augmentation therapy in AATD bronchiectasis in patients without pulmonary emphysema is not clear. We present the case of a 53-year-old woman (never smoker) who presented with increased susceptibility to infection, productive cough, and intermittent hemoptysis. Pulmonary function testing revealed restrictive impairment [VC 2,7 l (83% of pred.), FEV1 2,3 l (86% of pred.)]. A CT scan of the chest showed marked basal bronchiectasis with mucoid impaction, surrounding consolidation, and no emphysema. Despite frequent use of inhalation therapy, a satisfactory control of symptoms and exacerbations was not achieved. In the course of extended diagnostics regarding the genesis of bronchiectasis, a reduced alpha-1-antitrypsin (AAT) serum level was detected, and a genetic test revealed a homozygous Pi*ZZ genotype. We started augmentation therapy with AAT (Respreeza®, CLS Behring) at the dose of 60 mg/kg per week; the therapy was well tolerated by the patient, and she reported clinical improvement with a reduction in exacerbation frequency. AAT is a serine protease inhibitor and plays a major role in regulating inflammatory activities, in particular by inhibiting neutrophil elastase (NE). The present case illustrates the positive effect of augmentation therapy, including patients without airway obstruction. Among other causes, AATD should be considered as a possible cause of bronchiectasis, and the effects of augmentation therapy for this indication need to be prospectively studied.

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks.

Background: Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM. Methods: A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity). Results: The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001-0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6-0.91) and 0.90 (IQR = 0.85-0.94) in the institutional as well as 0.79 (IQR = 0.67-0.82) and 0.84 (IQR = 0.76-0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63-0.92) and 0.79 (IQR = 0.63-0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76-0.94) and 0.76 (IQR = 0.68-0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92-0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72-0.91) in the external test dataset. Conclusion: The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine.

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint LesionTM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

Library of molecular associations: curating the complex molecular basis of liver diseases.

BACKGROUND: Systems biology approaches offer novel insights into the development of chronic liver diseases. Current genomic databases supporting systems biology analyses are mostly based on microarray data. Although these data often cover genome wide expression, the validity of single microarray experiments remains questionable. However, for systems biology approaches addressing the interactions of molecular networks comprehensive but also highly validated data are necessary. RESULTS: We have therefore generated the first comprehensive database for published molecular associations in human liver diseases. It is based on PubMed published abstracts and aimed to close the gap between genome wide coverage of low validity from microarray data and individual highly validated data from PubMed. After an initial text mining process, the extracted abstracts were all manually validated to confirm content and potential genetic associations and may therefore be highly trusted. All data were stored in a publicly available database, Library of Molecular Associations http://www.medicalgenomics.org/databases/loma/news, currently holding approximately 1260 confirmed molecular associations for chronic liver diseases such as HCC, CCC, liver fibrosis, NASH/fatty liver disease, AIH, PBC, and PSC. We furthermore transformed these data into a powerful resource for molecular liver research by connecting them to multiple biomedical information resources. CONCLUSION: Together, this database is the first available database providing a comprehensive view and analysis options for published molecular associations on multiple liver diseases.

An undifferentiated carcinoma at Klatskin-position with long-term complete remission after chemotherapy.

BACKGROUND: Neoplasms anatomically adjacent to the bile duct usually derive from malignantly transformed cholangiocytes forming cholangiocarcinoma (CCA). CCAs are divided in extrahepatic (eCCA) and intrahepatic (iCCA) tumors. Patients with irresectable CCAs are treated with systemic chemotherapy and have an unfavorable prognosis with a median survival of about one year. Here, we report a case of an undifferentiated carcinoma in Klatskin-position with long-term remission after systemic chemotherapy. CASE PRESENTATION: A 65-year-old Caucasian male presented with painless jaundice caused by an undifferentiated carcinoma in Klatskin-position (Type IIIb). Alpha fetoprotein (AFP; 3675 IU/mL) and carbohydrate antigen 19-9 (CA 19-9; 183 U/ml) were elevated. An exploratory laparotomy was carried out, but the patient was found to be irresectable due to severe fibrosis caused by biliary obstruction. Histology showed an undifferentiated carcinoma with high proliferation rate, and the patient was therefore subjected to poly-chemotherapy treatment according to the FOLFOX6-protocol. During therapy, AFP decreased to normal. Subsequent CT scans and ERC revealed a complete remission. Four years past initial diagnosis, a new suspicious lesion in the liver is visible on MRT; however, AFP and CA 19-9 are still in the normal range. CONCLUSIONS: Our case demonstrates that histopathological defined diagnosis may significantly inform therapeutic decision-making in irresectable cholangiocarcinoma even in regard to conventional systemic therapy. In case of an undifferentiated carcinoma poly-chemotherapy may provide significant success.

Liver cancers with stem/progenitor-cell features - a rare chemotherapy-sensitive malignancy.

Primary liver tumors are a heterogeneous group of malignancies. Besides classical hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), combined and intermediate forms of liver cancer exist and can express stem-cell markers like nuclear cell adhesion molecule (NCAM-1/CD56), c-kit (CD117) or epithelial cell adhesion molecule (EpCAM) together with high proliferative activity. Liver tumors with progenitor-cell features are associated with an unfavorable prognosis, but the phenotype has not resulted in therapeutic consequences so far. We report three patients with liver cancers with stem/progenitor-cell features that responded exceptionally well to chemotherapy. These encouraging results indicate that the identification of liver cancer with stem/progenitor-cell phenotype in a patient´s tumor might justify an attempt to treat the patient with chemotherapy. Further case studies and finally clinical trials will be necessary to determine the optimal treatment for patients with this rare form of liver cancer.