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BACKGROUND/AIM Surgical resection for soft tissue sarcomas (STSs) is the gold standard for a curative oncologic therapy in combination with neoadjuvant or adjuvant radiation therapy (NRT/ART). The aim of this study was to determine prognostic factors influencing the survival of patients with STS undergoing NRT or ART considering various parameters in a retrospective, single-centre analysis over 15 years. PATIENTS AND METHODS We included 119 patients (male 59) and the median follow-up period was 69 months (4-197). The patients received NRT (n=64) or ART (n=55). We recorded the histopathologic subtype of STS, tumour grade, localization, tumour margins, complications, survival, local recurrence, and metastases. Survival analysis was performed using the Kaplan-Meier method. RESULTS The overall survival rate was 68.9% at 5 years. The localization (epifascial/subfascial), resection margin and type of radiation therapy (NRT/ART) had no significant impact on survival. Tumour grade, tumour size, local recurrence and metastases were significantly correlated with patient survival (p

Purpose In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)–positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). Methods Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. Results Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node–specific decision curve analysis, there was a clinical net benefit above LN short diameter. Conclusion The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.

Purpose To develop and validate a CT-based radiomics signature for the prognosis of loco-regional tumour control (LRC) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated by primary radiochemotherapy (RCTx) based on retrospective data from 6 partner sites of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Material and methods Pre-treatment CT images of 318 patients with locally advanced HNSCC were collected. Four-hundred forty-six features were extracted from each primary tumour volume and then filtered through stability analysis and clustering. First, a baseline signature was developed from demographic and tumour-associated clinical parameters. This signature was then supplemented by CT imaging features. A final signature was derived using repeated 3-fold cross-validation on the discovery cohort. Performance in external validation was assessed by the concordance index (C-Index). Furthermore, calibration and patient stratification in groups with low and high risk for loco-regional recurrence were analysed. Results For the clinical baseline signature, only the primary tumour volume was selected. The final signature combined the tumour volume with two independent radiomics features. It achieved moderately good discriminatory performance (C-Index [95% confidence interval]: 0.66 [0.55–0.75]) on the validation cohort along with significant patient stratification (p = 0.005) and good calibration. Conclusion We identified and validated a clinical-radiomics signature for LRC of locally advanced HNSCC using a multi-centric retrospective dataset. Prospective validation will be performed on the primary cohort of the HNpradBio trial of the DKTK-ROG once follow-up is completed.

Purpose: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radio mics") may be able to predict the pathological complete response (pCR). Methods: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2 weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as

Background Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS. Methods The study was registered at ClinicalTrials.gov (number NCT03798795 ). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects. Findings Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone. Interpretation MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. Fund The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.

Purpose: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N=165) and center 2 (N=61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index. Results: In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; P=.009). Conclusions: This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.

Radiomics has shown great potential for outcome prognosis and presents a promising approach for improving personalized cancer treatment. In radiomic analyses, features of different complexity are extracted from clinical imaging datasets, which are correlated to the endpoints of interest using machine-learning approaches. However, it is generally unclear if more complex features have a higher prognostic value and show a robust performance in external validation. Therefore, in this study, we developed and validated radiomic signatures for outcome prognosis after neoadjuvant radiochemotherapy in locally advanced rectal cancer (LARC) using computed tomography (CT) and T2-weighted magnetic resonance imaging (MRI) of two independent institutions (training/validation: 94/28 patients). For the prognosis of tumor response and freedom from distant metastases (FFDM), we used different imaging features extracted from the gross tumor volume: less complex morphological and first-order (MFO) features, more complex second-order texture (SOT) features, and both feature classes combined. Analyses were performed for both imaging modalities separately and combined. Performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumor response and FFDM, respectively. Overall, radiomic features showed prognostic value for both endpoints. Combining MFO and SOT features led to equal or higher performance in external validation compared to MFO and SOT features alone. The best results were observed after combining MRI and CT features (AUC = 0.76, CI = 0.65). In conclusion, promising biomarker signatures combining MRI and CT were developed for outcome prognosis in LARC. Further external validation is pending before potential clinical application.

Radiohybrid prostate-specific membrane antigen (rhPSMA) ligands are a new class of prostate cancer theranostic agents. F-18-rhPSMA-7 offers the advantages of F-18 labeling and low urinary excretion compared with 68Ga-PSMA-11. Here, we compare the frequency of non-tumor-related uptake and tumor positivity with Ga-68-PSMA-11 and F-18-rhPSMA-7 in patients with primary or recurrent prostate cancer. Methods: This retrospective matched-pair comparison matched 160 F-18-rhPSMA-7 with 160 Ga-68-PSMA-11 PET/CT studies for primary staging (n = 33) and biochemical recurrence (n = 127) according to clinical characteristics. Two nuclear medicine physicians reviewed all scans, first identifying all PET-positive lesions and then differentiating lesions suggestive of prostate cancer from those that were benign, on the basis of known pitfalls and ancillary information from CT. For each region, the SUVmax of the lesion with the highest PSMA ligand uptake was noted. Tumor positivity rates were determined, and SUVmax was compared separately for each tracer. Results: F-18-rhPSMA-7 and Ga-68-PSMA-11 PET revealed 566 and 289 PSMA ligand-positive lesions, respectively. Of these, 379 and 100 lesions, equaling 67.0% and 34.6%, respectively, of all PSMA-positive lesions, were considered benign. The distribution of their etiology was similar (42%, 24%, and 25% with 18F-rhPSMA-7 vs. 32%, 24%, and 38% with Ga-68-PSMA-1 1 for ganglia, bone, and unspecific lymph nodes, respectively). All primary tumors were positive with both agents (n = 33 each), whereas slightly more metastatic lesions were observed with Ga-68-PSMA-11 in both disease stages (113 for F-18-rhPSMA-7 and 124 for Ga-68-PSMA-11). The SUVmax of F-18-rhPSMA-7 and Ga-68-PSMA-1 1 did not differ (P > 0.05) in local recurrence or primary prostate cancer; however, the tumor-to-bladder ratio was significantly higher with 18F-rhPSMA-7 (4.9 +/- 5.3 vs. 2.2 +/- 3.7, P = 0.02, for local recurrence; 9.8 +/- 9.7 vs. 2.3 +/- 2.6, P < 0.001, for primary prostate cancer). Conclusion: The tumor positivity rate was consistently high for Ga-68-PSMA-1 1 and F-18-rhPSMA-7. Both tracers revealed a considerable number of areas of uptake that were reliably identified as benign by trained physicians making use of corresponding morphologic imaging and known PSMA pitfalls. These were more frequent with F-18-rhPSMA-7. However, the matched-pair comparison could have introduced a source of bias. Adequate reader training can allow physicians to differentiate benign uptake from disease and be able to benefit from the logistical and clinical advantages of F-18-rhPSMA-7.

Medical imaging plays an imminent role in today's radiation oncology workflow. Predominantly based on semantic image analysis, malignant tumors are diagnosed, staged, and therapy decisions are made. The field of "radiomics" promises to extract complementary, objective information from medical images. In radiomics, predefined quantitative features including intensity statistics, texture, shape, or filtering techniques are combined into statistical or machine learning models to predict clinical or biological outcomes. Alternatively, deep neural networks can directly analyze medical images and provide predictions. A large number of research studies could demonstrate that radiomics prediction models may provide significant benefits in the radiation oncology workflow including diagnostics, tumor characterization, target volume segmentation, prognostic stratification, and prediction of therapy response or treatment-related toxicities. This chapter provides an overview of techniques within the radiomics toolbox, potential clinical application, and current limitations. A literature overview of four selected malignant entities including non-small cell lung cancer, head and neck squamous cell carcinomas, soft tissue sarcomas, and gliomas is given.

Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTV entire). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTV entire&nbsp, was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 &plusmn, 0.04 (mean &plusmn, std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 &plusmn, 0.02 and 0.64 &plusmn, 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 &plusmn, 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend (p = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.

The aim of this study was to evaluate prognostic factors in patients with lung metastases who undergo lung stereotactic body radiotherapy (SBRT). A total of 87 patients with 129 lung metastases who underwent SBRT between November 2004 and May 2012 were enrolled in this retrospective study. The patient collective consisted of 54 men (62.1%) and 33 women (37.9%); the median age was 65 years (range 36–88). The Karnofsky performance index was ≥70% (median 90%) for all cases, but one (60%). Adverse effects were categorized using the CTCAE 4.0 classification system. Retrospective analyses regarding patients’ characteristics, progression-free survival (PFS), overall survival (OS), disease-specific survival (DSS), and local tumor control rates (LTC) were performed. On univariate and multivariate analysis OS, DSS, and PFS were significantly (p

Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients’ characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients’ death and disease progression at 2 years. Pre-treatment and treatment models were compared. The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression. A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.

Introduction Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. Methods After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. Results Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. Discussion Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. Conclusion This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Background There are different contouring guidelines for definition of the clinical target volume (CTV) for intensity-modulated radiation therapy (IMRT) of anal cancer (AC). We conducted a planning comparison study to evaluate and compare the dose to relevant organs at risk (OARs) while using different CTV definitions. Methods Twelve patients with a primary diagnosis of anal cancer, who were treated with primary chemoradiation (CRT), were selected. We generated four guideline-specific CTVs and subsequently planned target volumes (PTVs) on the planning CT scan of each patient. An IMRT plan for volumetric arc therapy (VMAT) was set up for each PTV. Dose parameters of the planned target volume (PTV) and OARs were evaluated and compared, too. Results The mean volume of the four PTVs ranged from 2138 cc to 2433 cc. The target volumes contoured by the authors based on the recommendations of each group were similar in the pelvis, while they differed significantly in the inguinal region. There were no significant differences between the four target volumes with regard to the dose parameters of the cranially located OARs. Conversely, some dose parameters concerning the genitals and the skin varied significantly among the different guidelines. Conclusion The four contouring guidelines differ significantly concerning the inguinal region. In order to avoid inguinal recurrence and to protect relevant OARs, further investigations are needed to generate uniform standards for definition of the elective clinical target volume in the inguinal region.

Cancer Medicine published by John Wiley & Sons Ltd. BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, pathological, semantic MRI-based, and FET-PET/CT-derived information. Finally, the value of adding treatment features was evaluated. METHODS: One hundred and eighty-nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET-PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI-based," and "FET-PET/CT-based" models, as well as combinations. Treatment features were combined with all other features. RESULTS: Of all single feature class models, the MRI-based model had the highest prediction performance on the validation set for OS (C-index: 0.61 [95% confidence interval: 0.51-0.72]) and PFS (C-index: 0.61 [0.50-0.72]). The combination of all features did increase performance above all single feature class models up to C-indices of 0.70 (0.59-0.84) and 0.68 (0.57-0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C-indices of 0.73 (0.62-0.84) and 0.71 (0.60-0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. CONCLUSIONS: MRI-based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

Purpose Glioblastoma is routinely treated by concomitant radiochemotherapy. Current target definition guidelines use anatomic MRI (magnetic resonance imaging) scans, taking into account contrast enhancement and the rather unspecific hyperintensity on the fluid-attenuated inversion recovery (FLAIR) sequence. Methods and materials We applied deep learning based free water correction of diffusion tensor imaging (DTI) scans to estimate the infiltrative gross tumor volume (iGTV) inside of the FLAIR hyperintense region. We analyzed the resulting iGTVs and their impact on target volume definition in a retrospective cohort of 33 GBM patients. Results iGTVs were significantly smaller compared to standard pre- and post-operative gross tumor volume (GTV) definitions. Two novel infiltrative tumor GTVs (nGTVPRE-OP and nGTVPOST-OP) defined as the conjunction volume of the standard GTV and the iGTV showed only a moderate increase in size compared to standard GTV definitions. On postoperative scans, the iGTV was predominantly covered by the two clinical target volume (CTV) concepts CTVEORTC and CTVROTG1. A novel infiltrative tumor CTV (nCTV) [nGTVPOST-OP + 2 cm margin] was significantly smaller compared to CTVROTG1 but larger than CTVEORTC. The overlap volume and conformity index demonstrated a distinct spatial configuration of the nCTV. Tumor recurrences overlapped with the iGTV in all but one patients and were completely covered by the nCTV in all patients. After reducing the margin to 1 cm recurrences coverage was at least in-field in all patients. Conclusion To conclude, free water corrected DTI scans may help to define infiltrative tumor areas of GBM that could ultimately be used to individualize RT treatment planning in terms of dose sparing or dose escalation.

Purpose In soft tissue sarcoma (STS) patients systemic progression and survival remain comparably low despite low local recurrence rates. In this work, we investigated whether quantitative imaging features (“radiomics”) of radiotherapy planning CT-scans carry a prognostic value for pre-therapeutic risk assessment. Methods CT-scans, tumor grade, and clinical information were collected from three independent retrospective cohorts of 83 (TUM), 87 (UW) and 51 (McGill) STS patients, respectively. After manual segmentation and preprocessing, 1358 radiomic features were extracted. Feature reduction and machine learning modeling for the prediction of grading, overall survival (OS), distant (DPFS) and local (LPFS) progression free survival were performed followed by external validation. Results Radiomic models were able to differentiate grade 3 from non-grade 3 STS (area under the receiver operator characteristic curve (AUC): 0.64). The Radiomic models were able to predict OS (C-index: 0.73), DPFS (C-index: 0.68) and LPFS (C-index: 0.77) in the validation cohort. A combined clinical-radiomics model showed the best prediction for OS (C-index: 0.76). The radiomic scores were significantly associated in univariate and multivariate cox regression and allowed for significant risk stratification for all three endpoints. Conclusion This is the first report demonstrating a prognostic potential and tumor grading differentiation by CT-based radiomics.

For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information. 189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan–Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined. For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. “Multilocality,” “deep white-matter invasion,” “satellites,” and “ependymal invasion” were over proportionally selected for multivariate model generation, underlining their importance. We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

Hyperthermia is one of the hot topics that have been discussed over decades. However, it never made its way into primetime. The basic biological rationale of heat to enhance the effect of radiation, chemotherapeutic agents and immunotherapy is evident. Preclinical work has confirmed this effect. Hyperthermia may trigger changes in perfusion and oxygenation as well as inhibition of DNA repair mechanisms. Moreover, there is evidence for immune stimulation and the induction of systemic immune responses. Despite the increasing number of solid clinical studies only few centers have included this adjuvant treatment into their repertoire. Over the years, abundant prospective and randomized clinical data have emerged demonstrating a clear benefit of combined hyperthermia and radiotherapy for multiple entities such as superficial breast cancer recurrences, cervix carcinoma or cancers of the head and neck. Regarding less investigated indications, the existing data is promising and more clinical trials are currently recruiting patients. How do we proceed from here? Preclinical evidence is present. Multiple indications benefit from additional hyperthermia in the clinical setting. This article summarizes the present evidence, and develops ideas for future research.

Mutations in the transcription factor NF-E2 in patients with myeloproliferative neoplasms result in a truncated protein that enhances the function of wild-type NF-E2 and causes erythrocytosis and throbocytosis in a mouse model., The molecular etiology of myeloproliferative neoplasms (MPNs) remains incompletely understood, despite recent advances incurred through the discovery of several different mutations in MPN patients. We have recently described overexpression of the transcription factor NF-E2 in MPN patients and shown that elevated NF-E2 levels in vivo cause an MPN phenotype and predispose to leukemic transformation in transgenic mice. We report the presence of acquired insertion and deletion mutations in the NF-E2 gene in MPN patients. These result in truncated NF-E2 proteins that enhance wild-type (WT) NF-E2 function and cause erythrocytosis and thrombocytosis in a murine model. NF-E2 mutant cells acquire a proliferative advantage, witnessed by clonal dominance over WT NF-E2 cells in MPN patients. Our data underscore the role of increased NF-E2 activity in the pathophysiology of MPNs.