European association of urology risk stratification predicts outcome in patients receiving PSMA-PET-planned salvage radiotherapy for biochemical recurrence following radical prostatectomy

PURPOSE

The European Association of Urology (EAU) proposed a risk stratification (high vs. low risk) for patients with biochemical recurrence (BR) following radical prostatectomy (RP). Here we investigated whether this stratification accurately predicts outcome, particularly in patients staged with PSMA-PET.

METHODS

For this study, we used a retrospective database including 1222 PSMA-PET-staged prostate cancer patients who were treated with salvage radiotherapy (SRT) for BR, at 11 centers in 5 countries. Patients with lymph node metastases (pN1 or cN1) or unclear EAU risk group were excluded. The remaining cohort comprised 526 patients, including 132 low-risk and 394 high-risk patients.

RESULTS

The median follow-up time after SRT was 31.0 months. The 3-year biochemical progression-free survival (BPFS) was 85.7 % in EAU low-risk versus 69.4 % in high-risk patients (p = 0.002). The 3-year metastasis-free survival (MFS) was 94.4 % in low-risk versus 87.6 % in high-risk patients (p = 0.005). The 3-year overall survival (OS) was 99.0 % in low-risk versus 99.6 % in high-risk patients (p = 0.925). In multivariate analysis, EAU risk group remained a statistically significant predictor of BPFS (p = 0.003, HR 2.022, 95 % CI 1.262-3.239) and MFS (p = 0.013, HR 2.986, 95 % CI 1.262-7.058).

CONCLUSION

Our data support the EAU risk group definition. EAU risk grouping for BCR reliably predicted outcome in patients staged lymph node-negative after RP and with PSMA-PET before SRT. To our knowledge, this is the first study validating the EAU risk grouping in patients treated with PSMA-PET-planned SRT.

Tumor Contact With Internal Mammary Perforator Vessels as Risk Factor for Gross Internal Mammary Lymph Node Involvement in Patients With Breast Cancer

PURPOSE

The identification of internal mammary lymph node metastases and the assessment of associated risk factors are crucial for adjuvant regional lymph node irradiation in patients with breast cancer. The current study aims to investigate whether tumor contact with internal mammary perforator vessels is associated with gross internal mammary lymph node involvement.

METHODS AND MATERIALS

We included 297 patients with primary breast cancer and gross internal mammary (IMN+) and/or axillary metastases as well as 230 patients without lymph node metastases. Based on pretreatment dynamic contrast-enhanced magnetic resonance imaging, we assessed contact of the tumor with the internal mammary perforating vessels (IMPV).

RESULTS

A total of 59 patients had ipsilateral IMN+ (iIMN+), 10 patients had contralateral IMN+ (cIMN+), and 228 patients had ipsilateral axillary metastases without IMN; 230 patients had node-negative breast cancer. In patients with iIMN+, 100% of tumors had contact with ipsilateral IMPV, with 94.9% (n = 56) classified as major contact. In iIMN- patients, major IMPV contact was observed in only 25.3% (n = 116), and 36.2% (n = 166) had no IMPV contact at all. Receiver operating characteristic analysis revealed that "major IMPV contact" was more accurate in predicting iIMN+ (area under the curve, 0.85) compared with a multivariate model combining grade of differentiation, tumor site, size, and molecular subtype (area under the curve, 0.65). Strikingly, among patients with cIMN+, 100% of tumors had contact with a crossing contralateral IMPV, whereas in cIMN- patients, IMPVs to the contralateral side were observed in only 53.4% (iIMN+) and 24.8% (iIMN-), respectively.

CONCLUSIONS

Tumor contact with the IMPV is highly associated with risk of gross IMN involvement. Further studies are warranted to investigate whether this identified risk factor is also associated with microscopic IMN involvement and whether it can assist in the selection of patients with breast cancer for irradiation of the internal mammary lymph nodes.

Prostate cancer and elective nodal radiation therapy for cN0 and pN0-a never ending story? : Recommendations from the prostate cancer expert panel of the German Society of Radiation Oncology (DEGRO)

For prostate cancer, the role of elective nodal irradiation (ENI) for cN0 or pN0 patients has been under discussion for years. Considering the recent publications of randomized controlled trials, the prostate cancer expert panel of the German Society of Radiation Oncology (DEGRO) aimed to discuss and summarize the current literature. Modern trials have been recently published for both treatment-naïve patients (POP-RT trial) and patients after surgery (SPPORT trial). Although there are more reliable data to date, we identified several limitations currently complicating the definitions of general recommendations. For patients with cN0 (conventional or PSMA-PET staging) undergoing definitive radiotherapy, only men with high-risk factors for nodal involvement (e.g., cT3a, GS ≥ 8, PSA ≥ 20 ng/ml) seem to benefit from ENI. For biochemical relapse in the postoperative situation (pN0) and no PSMA imaging, ENI may be added to patients with risk factors according to the SPPORT trial (e.g., GS ≥ 8; PSA > 0.7 ng/ml). If PSMA-PET/CT is negative, ENI may be offered for selected men with high-risk factors as an individual treatment approach.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model

Background

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation.

Methods

One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans.

Results

The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans.

Conclusions

Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

The prognostic significance of a negative PSMA-PET scan prior to salvage radiotherapy following radical prostatectomy

AIM

The optimal management for early recurrent prostate cancer following radical prostatectomy (RP) in patients with negative prostate-specific membrane antigen positron-emission tomography (PSMA-PET) scan is an ongoing subject of debate. The aim of this study was to evaluate the outcome of salvage radiotherapy (SRT) in patients with biochemical recurrence with negative PSMA PET finding.

METHODS

This retrospective, multicenter (11 centers, 5 countries) analysis included patients who underwent SRT following biochemical recurrence (BR) of PC after RP without evidence of disease on PSMA-PET staging. Biochemical recurrence-free survival (bRFS), metastatic-free survival (MFS) and overall survival (OS) were assessed using Kaplan-Meier method. Multivariable Cox proportional hazards regression assessed predefined predictors of survival outcomes.

RESULTS

Three hundred patients were included, 253 (84.3%) received SRT to the prostate bed only, 46 (15.3%) additional elective pelvic nodal irradiation, respectively. Only 41 patients (13.7%) received concomitant androgen deprivation therapy (ADT). Median follow-up after SRT was 33 months (IQR: 20-46 months). Three-year bRFS, MFS, and OS following SRT were 73.9%, 87.8%, and 99.1%, respectively. Three-year bRFS was 77.5% and 48.3% for patients with PSA levels before PSMA-PET ≤ 0.5 ng/ml and > 0.5 ng/ml, respectively. Using univariate analysis, the International Society of Urological Pathology (ISUP) grade > 2 (p = 0.006), metastatic pelvic lymph nodes at surgery (p = 0.032), seminal vesicle involvement (p < 0.001), pre-SRT PSA level of > 0.5 ng/ml (p = 0.004), and lack of concomitant ADT (p = 0.023) were significantly associated with worse bRFS. On multivariate Cox proportional hazards, seminal vesicle infiltration (p = 0.007), ISUP score >2 (p = 0.048), and pre SRT PSA level > 0.5 ng/ml (p = 0.013) remained significantly associated with worse bRFS.

CONCLUSION

Favorable bRFS after SRT in patients with BR and negative PSMA-PET following RP was achieved. These data support the usage of early SRT for patients with negative PSMA-PET findings.

Identifying core MRI sequences for reliable automatic brain metastasis segmentation

BACKGROUND

Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation.

METHODS

We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 339 patients with BMs from seven centers. A baseline 3D U-Net with all four sequences and six U-Nets with plausible sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE + T2-FLAIR, T1-CE + T1 + T2-FLAIR, T1-CE + T1) were trained on 239 patients from two centers and subsequently tested on an external cohort of 100 patients from five centers.

RESULTS

The model based on T1-CE alone achieved the best segmentation performance for BM segmentation with a median Dice similarity coefficient (DSC) of 0.96. Models trained without T1-CE performed worse (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For edema segmentation, models that included both T1-CE and T2-FLAIR performed best (DSC = 0.93), while the remaining four models without simultaneous inclusion of these both sequences reached a median DSC of 0.81-0.89.

CONCLUSIONS

A T1-CE-only protocol suffices for the segmentation of BMs. The combination of T1-CE and T2-FLAIR is important for edema segmentation. Missing either T1-CE or T2-FLAIR decreases performance. These findings may improve imaging routines by omitting unnecessary sequences, thus allowing for faster procedures in daily clinical practice while enabling optimal neural network-based target definitions.

The importance of planning CT-based imaging features for machine learning-based prediction of pain response

Patients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans. Radiomics, semantic and clinical features were collected for all patients. Random forest (RFC) and support vector machine (SVM) classifiers were compared using repeated nested cross-validation. The best radiomics classifier was trained on CTV with an area under the receiver-operator curve (AUROC) of 0.62 ± 0.01 (RFC; 95% confidence interval). The semantic model achieved a comparable AUROC of 0.63 ± 0.01 (RFC), significantly below the clinical model (SVM, AUROC: 0.80 ± 0.01); and slightly lower than the spinal instability neoplastic score (SINS; LR, AUROC: 0.65 ± 0.01). A combined model did not improve performance (AUROC: 0,74 ± 0,01). We could demonstrate that radiomics and semantic analyses of planning CTs allowed for limited prediction of therapy response to palliative RT. ML predictions based on established clinical parameters achieved the best results.

Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients

Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22-0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18-0.34 and 0.18-0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.

What MRI Sequences are Necessary for Automated Neural Network-Based Metastasis Segmentation - An Ablation Study

PURPOSE/OBJECTIVE(S)

Brain metastasis (BM) delineation is a time-consuming process in both daily clinical practice and research. Automated BM segmentation algorithms can be used to assist in this task. Most approaches to brain tumor segmentation, such as algorithms trained on the BraTS challenge, use four magnetic resonance imaging (MRI) sequences as input, making them susceptible to missing or corrupted sequences and increase the number of sequences necessary for MRI RT planning. The goal of this project is to compare neural networks with different combinations of input sequences for the segmentation of the contrast-enhancing metastasis and the surrounding FLAIR hyperintense edema. All models were tested in a multicenter international external test cohort. This allows us to determine which MRI sequences are needed for effective automated segmentations.

MATERIALS/METHODS

In total, we had T1-weighted sequences without (T1) and with contrast enhancement (T1-CE), T2-weighted sequences (T2), and T2 fluid-attenuated inversion recovery (FLAIR) sequences from 339 patients with at least one brain metastasis from seven centers available. Preprocessing yielded co-registered, skull-stripped sequences with an isotropic resolution of 1 millimeter. The contrast-enhancing metastasis as well as the surrounding FLAIR hyperintense edema were manually segmented to create reference labels. A baseline 3D U-Net with all four sequences as well as six additional U-Nets with different clinically plausible combinations (T1-CE; T1; FLAIR; T1-CE+FLAIR; T1-CE+T1+FLAIR; T1-CE+T1) of input sequences were trained on a cohort of 239 patients from two centers and subsequently tested on an external cohort of 100 patients from the remaining five centers.

RESULTS

All models that included T1-CE in their selected sequences showed similar performance for metastasis segmentation with a median Dice similarity coefficient (DSC) of 0.93-0.96. T1-CE alone likewise achieved a performance of 0.96 (IQR 0.93-0.97). The model trained with only FLAIR performed worse (DSC = 0.73, IQR 0.54-0.84). For edema segmentation, models that included both T1-CE and FLAIR performed best (median DSC = 0.93), while the remaining four models without simultaneous inclusion of these two sequences (T1-CE; T1; FLAIR; T1-CE+T1) reached a median DSC of 0.81-0.89.

CONCLUSION

Automatic segmentation of brain metastases with less than four input sequences is feasible with minimal or no loss of quality. A T1-CE-only protocol suffices for metastasis segmentation. In contrast, for edema segmentation, the combination of T1-CE and FLAIR seems to be important. Missing either T1-CE or FLAIR decreases performance. These findings may improve future imaging routines by omitting unnecessary sequences, thus speeding up procedures in daily clinical practice while allowing for optimal neural network-based target definitions.

The Value of Equivalent Dose Calculation for Dosiomics and Radiomics-Based Prediction of Pneumonitis after Thoracic Radiotherapy with Immune Checkpoint Inhibition

PURPOSE/OBJECTIVE(S)

Post-therapy pneumonitis (PTP) is a relevant side effect after thoracic radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICI). The impact of the combination of both is unclear. We aim to improve risk estimation by prediction of PTP with and without ICI therapy. To analyze the influence of different fractionation schemes, the value of voxel-wise 2 Gy equivalent dose (EQD2) is investigated.

MATERIALS/METHODS

Clinical data from 100 patients who received fractionated RT (single dose ≤ 3Gy) RT were collected. 36 patients received additional ICI therapy. PTP of all grades were monitored. Planning Computed tomographies (CTs), segmentations and 3D dose data were extracted and converted to EQD2. Dosiomics and radiomics features were extracted using 1000-fold bootstrapping using Pearson intercorrelation and the Boruta algorithm for 5 single and 4 combined predictive models. Machine learning algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using a 5-fold nested cross validation approach and Synthetic Minority Oversampling Technique resampling in R. Analysis was performed using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

All investigated models predicted PTP better than random (AUC>.5) (Table 1). Dosiomics+Radiomics models based on EQD2 using rf classifier resulted in the highest predictive performance (AUC = .83 (95% Confidence Interval .83-.84)) and performed worse on physical dose data (AUC = .72 (.71-.73)). For single models, radiomics and dosiomics achieved the best prediction (AUC = .73 (.72-.74), AUC = .8 (.79-.81)) for physical dose and EQD2, respectively. Clinical factors and ICI therapy (AUC = .6 (.59-.62)) had minor impact on PTP prediction. Table 1: AUC and 95% confidence intervals (CI) for all investigated Machine Learning models for EQD2 and physical doses (D).

CONCLUSION

Dosiomics+Radiomics machine learning models have strong capability of PTP prediction and could contribute to pre-treatment decision making. Fractionation schemes should be considered for dose-based prediction strategies. Additional ICI therapy has limited impact on PTP prediction.

Post-Operative Stereotactic Radiotherapy for Resected Brain Metastases: Results of the Multicenter Analysis (AURORA) of the German Working Group "Stereotactic Radiotherapy"

PURPOSE/OBJECTIVE(S)

While the results of prospective studies support the use of postoperative stereotactic radiotherapy (RT) to the resection cavity (RC) as the standard of care after surgery, there are several issues that need to be investigated such as factors for improving local control, risk of leptomeningeal disease and radiation necrosis. Further, the optimal dose and fractionation is still under debate.

MATERIALS/METHODS

The working group "Stereotactic Radiotherapy" of the German Society of Radiation Oncology (DEGRO) analyzed its multi-institutional database with 661 patients who received postoperative stereotactic RT to the RC. Treatment was performed at 13 centers between 2008 and 2021. Patient characteristics, treatment details, and follow-up data including overall survival (OS), local control (LC) were evaluated. Cox Regression and Kaplan-Meier curves with Log-rank Tests were calculated for selected variables.

RESULTS

In this retrospective study, overall survival was 61.5% at 1 year, 47.6% at 2 years, and 35.5% at 3 years, and local control was 84.6% at 1 year, 74.8% at 2 years, and 72.8% at 3 years. 96% of patients were treated with hypofractionated stereotactic radiotherapy (HSRT), only 26 patients received single fraction radiosurgery (4%). Prognostic factors associated with overall survival were Karnofsky Performance Status, RPA and GPA class, controlled primary tumor and absence of extracranial metastases, whereas prognostic factor associated with local control was planning target volume (23 mL or less).

CONCLUSION

HSRT is the most common fractionation form in the treatment of RCs in this multicenter analysis. This approach results in excellent OS and LC outcomes. OS in patients with resected brain metastases is mainly influenced by performance status. In regard to local control, RT of large cavities remain a challenge with significantly worse outcome.

Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

PURPOSE

To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET).

MATERIAL AND METHODS

Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach.

RESULTS

Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature.

CONCLUSION

This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.

PSMA-PET/CT-guided salvage radiotherapy in recurrent or persistent prostate cancer and PSA < 0.2 ng/ml

PURPOSE

The purpose of this retrospective, multicenter study was to assess efficacy of PSMA-PET/CT-guided salvage radiotherapy (sRT) in patients with recurrent or persistent PSA after primary surgery and PSA levels < 0.2 ng/ml.

METHODS

The study included patients from a pooled cohort (n = 1223) of 11 centers from 6 countries. Patients with PSA levels > 0.2 ng/ml prior to sRT or without sRT to the prostatic fossa were excluded. The primary study endpoint was biochemical recurrence-free survival (BRFS) and BR was defined as PSA nadir after sRT + 0.2 ng/ml. Cox regression analysis was performed to assess the impact of clinical parameters on BRFS. Recurrence patterns after sRT were analyzed.

RESULTS

The final cohort consisted of 273 patients; 78/273 (28.6%) and 48/273 (17.6%) patients had local or nodal recurrence on PET/CT. The most frequently applied sRT dose to the prostatic fossa was 66-70 Gy (n = 143/273, 52.4%). SRT to pelvic lymphatics was delivered in 87/273 (31.9%) patients and androgen deprivation therapy was given to 36/273 (13.2%) patients. After a median follow-up time of 31.1 months (IQR: 20-44), 60/273 (22%) patients had biochemical recurrence. The 2- and 3-year BRFS was 90.1% and 79.2%, respectively. The presence of seminal vesicle invasion in surgery (p = 0.019) and local recurrences in PET/CT (p = 0.039) had a significant impact on BR in multivariate analysis. In 16 patients, information on recurrence patterns on PSMA-PET/CT after sRT was available and one had recurrent disease inside the RT field.

CONCLUSION

This multicenter analysis suggests that implementation of PSMA-PET/CT imaging for sRT guidance might be of benefit for patients with very low PSA levels after surgery due to promising BRFS rates and a low number of relapses within the sRT field.

Salvage radiotherapy is effective in patients with PSMA-PET-negative biochemical recurrence- results of a retrospective study

BACKGROUND

/Purpose: The present study aimed to assess whether SRT to the prostatic fossa should be initiated in a timely manner after detecting biochemical recurrence (BR) in patients with prostate cancer, when no correlate was identified with prostate-specific membrane antigen positron emission tomography (PSMA-PET).

MATERIALS AND METHODS

This retrospective, multicenter analysis included 1222 patients referred for PSMA-PET after a radical prostatectomy due to BR. Exclusion criteria were: pathological lymph node metastases, prostate-specific antigen (PSA) persistence, distant or lymph node metastases, nodal irradiation, and androgen deprivation therapy (ADT). This led to a cohort of 341 patients. Biochemical progression-free survival (BPFS) was the primary study endpoint.

RESULTS

The median follow-up was 28.0 months. The 3-year BPFS was 71.6% in PET-negative cases and 80.8% in locally PET-positive cases. This difference was significant in univariate (p=0.019), but not multivariate analyses (p=0.366, HR: 1.46, 95%CI: 0.64-3.32). The 3-year BPFS in PET-negative cases was significantly influenced by age (p=0.005), initial pT3/4 (p<0.001), pathology scores (ISUP) ≥3 (p=0.026), and doses to fossa >70 Gy (p=0.027) in univariate analyses. In multivariate analyses, only age (HR: 1.096, 95%CI: 1.023-1.175, p=0.009) and PSA-doubling time (HR: 0.339, 95%CI: 0.139-0.826, p=0.017) remained significant.

CONCLUSION

To our best knowledge, this study provided the largest SRT analysis in patients without ADT that were lymph node-negative on PSMA-PET. A multivariate analysis showed no significant difference in BPFS between locally PET-positive and PET-negative cases. These results supported the current EAU recommendation to initiate SRT in a timely manner after detecting BR in PET negative patients.

Development and Validation of a Multi-institutional Nomogram of Outcomes for PSMA-PET-Based Salvage Radiotherapy for Recurrent Prostate Cancer

Importance

Prostate-specific antigen membrane positron-emission tomography (PSMA-PET) is increasingly used to guide salvage radiotherapy (sRT) after radical prostatectomy for patients with recurrent or persistent prostate cancer.

Objective

To develop and validate a nomogram for prediction of freedom from biochemical failure (FFBF) after PSMA-PET-based sRT.

Design, Setting, and Participants

This retrospective cohort study included 1029 patients with prostate cancer treated between July 1, 2013, and June 30, 2020, at 11 centers from 5 countries. The initial database consisted of 1221 patients. All patients had a PSMA-PET scan prior to sRT. Data were analyzed in November 2022.

Exposures

Patients with a detectable post-radical prostatectomy prostate-specific antigen (PSA) level treated with sRT to the prostatic fossa with or without additional sRT to pelvic lymphatics or concurrent androgen deprivation therapy (ADT) were eligible.

Main Outcomes and Measures

The FFBF rate was estimated, and a predictive nomogram was generated and validated. Biochemical relapse was defined as a PSA nadir of 0.2 ng/mL after sRT.

Results

In the nomogram creation and validation process, 1029 patients (median age at sRT, 70 years [IQR, 64-74 years]) were included and further divided into a training set (n = 708), internal validation set (n = 271), and external outlier validation set (n = 50). The median follow-up was 32 months (IQR, 21-45 months). Based on the PSMA-PET scan prior to sRT, 437 patients (42.5%) had local recurrences and 313 patients (30.4%) had nodal recurrences. Pelvic lymphatics were electively irradiated for 395 patients (38.4%). All patients received sRT to the prostatic fossa: 103 (10.0%) received a dose of less than 66 Gy, 551 (53.5%) received a dose of 66 to 70 Gy, and 375 (36.5%) received a dose of more than 70 Gy. Androgen deprivation therapy was given to 325 (31.6%) patients. On multivariable Cox proportional hazards regression analysis, pre-sRT PSA level (hazard ratio [HR], 1.80 [95% CI, 1.41-2.31]), International Society of Urological Pathology grade in surgery specimen (grade 5 vs 1+2: HR, 2.39 [95% CI, 1.63-3.50], pT stage (pT3b+pT4 vs pT2: HR, 1.91 [95% CI, 1.39-2.67]), surgical margins (R0 vs R1+R2+Rx: HR, 0.60 [95% CI, 0.48-0.78]), ADT use (HR, 0.49 [95% CI, 0.37-0.65]), sRT dose (>70 vs ≤66 Gy: HR, 0.44 [95% CI, 0.29-0.67]), and nodal recurrence detected on PSMA-PET scans (HR, 1.42 [95% CI, 1.09-1.85]) were associated with FFBF. The mean (SD) nomogram concordance index for FFBF was 0.72 (0.06) for the internal validation cohort and 0.67 (0.11) in the external outlier validation cohort.

Conclusions and Relevance

This cohort study of patients with prostate cancer presents an internally and externally validated nomogram that estimated individual patient outcomes after PSMA-PET-guided sRT.

Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas

Background: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. Methods: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. Results: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60–70% accuracy, 55–80% sensitivity, and 63–77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. Conclusion: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.

Outcome of patients with soft tissue sarcomas of the extremities and trunk treated by (neo)adjuvant intensity modulated radiation therapy with curative intent

BACKGROUND

Soft tissue sarcomas (STS) are a relatively rare group of malignant tumors. Currently, there is very little published clinical data, especially in the context of curative multimodal therapy with image-guided, conformal, intensity-modulated radiotherapy.

METHODS

Patients who received preoperative or postoperative intensity-modulated radiotherapy for STS of the extremities or trunk with curative intent were included in this single centre retrospective analysis. A Kaplan-Meier analysis was performed to evaluate survival endpoints. Multivariable proportional hazard models were used to investigate the association between survival endpoints and tumour-, patient-, and treatment-specific characteristics.

RESULTS

86 patients were included in the analysis. The most common histological subtypes were undifferentiated pleomorphic high-grade sarcoma (UPS) (27) and liposarcoma (22). More than two third of the patients received preoperative radiation therapy (72%). During the follow-up period, 39 patients (45%) suffered from some type of relapse, mainly remote (31%). The two-years overall survival rate was 88%. The median DFS was 48 months and the median DMFS was 51 months. Female gender (HR 0.460 (0.217; 0.973)) and histology of liposarcomas compared to UPS proved to be significantly more favorable in terms of DFS (HR 0.327 (0.126; 0.852)).

CONCLUSION

Conformal, intensity-modulated radiotherapy is an effective treatment modality in the preoperative or postoperative management of STS. Especially for the prevention of distant metastases, the establishment of modern systemic therapies or multimodal therapy approaches is necessary.

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.

Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study

BACKGROUND

Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability.

METHODS

We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n = 88) including four centers.

RESULTS

Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07).

CONCLUSION

Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions.

Differentiating Enchondromas and Atypical Cartilaginous Tumors in Long Bones with Computed Tomography and Magnetic Resonance Imaging

The differentiation between the atypical cartilaginous tumor (ACT) and the enchondromas is crucial as ACTs require a curettage and clinical as well as imaging follow-ups, whereas in the majority of cases enchondromas require neither a treatment nor follow-ups. Differentiating enchondromas from ACTs radiologically remains challenging. Therefore, this study evaluated imaging criteria in a combination of computed tomography (CT) and magnetic resonance (MR) imaging for the differentiation between enchondromas and ACTs in long bones. A total of 82 patients who presented consecutively at our institution with either an ACT (23, age 52.7 ±18.8 years; 14 women) or an enchondroma (59, age 46.0 ± 11.1 years; 37 women) over a period of 10 years, who had undergone preoperative MR and CT imaging and subsequent biopsy or/and surgical removal, were included in this study. A histopathological diagnosis was available in all cases. Two experienced radiologists evaluated several imaging criteria on CT and MR images. Likelihood of an ACT was significantly increased if either edema within the bone (p = 0.049), within the adjacent soft tissue (p = 0.006) or continuous growth pattern (p = 0.077) were present or if the fat entrapment (p = 0.027) was absent on MR images. Analyzing imaging features on CT, the likelihood of the diagnosis of an ACT was significantly increased if endosteal scalloping >2/3 (p < 0.001), cortical penetration (p < 0.001) and expansion of bone (p = 0.002) were present and if matrix calcifications were observed in less than 1/3 of the tumor (p = 0.013). All other imaging criteria evaluated showed no significant influence on likelihood of ACT or enchondroma (p > 0.05). In conclusion, both CT and MR imaging show suggestive signs which can help to adequately differentiate enchondromas from ACTs in long bones and therefore can improve diagnostics and consequently patient management. Nevertheless, these features are rare and a combination of CT and MR imaging features did not improve the diagnostic performance substantially.

The maximum standardized uptake value in patients with recurrent or persistent prostate cancer after radical prostatectomy and PSMA-PET-guided salvage radiotherapy-a multicenter retrospective analysis

Purpose

This study aims to evaluate the association of the maximum standardized uptake value (SUVmax) in positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET) prior to salvage radiotherapy (sRT) on biochemical recurrence free survival (BRFS) in a large multicenter cohort.

Methods

Patients who underwent ⁶⁸ Ga-PSMA11-PET prior to sRT were enrolled in four high-volume centers in this retrospective multicenter study. Only patients with PET-positive local recurrence (LR) and/or nodal recurrence (NR) within the pelvis were included. Patients were treated with intensity-modulated-sRT to the prostatic fossa and elective lymphatics in case of nodal disease. Dose escalation was delivered to PET-positive LR and NR. Androgen deprivation therapy was administered at the discretion of the treating physician. LR and NR were manually delineated and SUVmax was extracted for LR and NR. Cox-regression was performed to analyze the impact of clinical parameters and the SUVmax-derived values on BRFS.

Results

Two hundred thirty-five patients with a median follow-up (FU) of 24 months were included in the final cohort. Two-year and 4-year BRFS for all patients were 68% and 56%. The presence of LR was associated with favorable BRFS (p = 0.016). Presence of NR was associated with unfavorable BRFS (p = 0.007). While there was a trend for SUVmax values ≥ median (p = 0.071), SUVmax values ≥ 75% quartile in LR were significantly associated with unfavorable BRFS (p = 0.022, HR: 2.1, 95%CI 1.1–4.6). SUVmax value in NR was not significantly associated with BRFS. SUVmax in LR stayed significant in multivariate analysis (p = 0.030). Sensitivity analysis with patients for who had a FU of > 12 months (n = 197) confirmed these results.

Conclusion

The non-invasive biomarker SUVmax can prognosticate outcome in patients undergoing sRT and recurrence confined to the prostatic fossa in PSMA-PET. Its addition might contribute to improve risk stratification of patients with recurrent PCa and to guide personalized treatment decisions in terms of treatment intensification or de-intensification.

This article is part of the Topical Collection on Oncology—Genitourinary.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05931-5.

A Unified 3D Framework for Organs-at-Risk Localization and Segmentation for Radiation Therapy Planning

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of 0.9260 ± 0.18% on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.

Self-Supervised Pretext Tasks in Model Robustness & Generalizability: A Revisit from Medical Imaging Perspective

Self-supervised pretext tasks have been introduced as an effective strategy when learning target tasks on small annotated data sets. However, while current research focuses on exploring novel pretext tasks for meaningful and reusable representation learning for the target task, the study of its robustness and generalizability has remained relatively under-explored. Specifically, it is crucial in medical imaging to proactively investigate performance under different perturbations for reliable deployment of clinical applications. In this work, we revisit medical imaging networks pre-trained with self-supervised learnings and categorically evaluate robustness and generalizability compared to vanilla supervised learning. Our experiments on pneumonia detection in X-rays and multi-organ segmentation in CT yield conclusive results exposing the hidden benefits of self-supervision pre-training for learning robust feature representations.

Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models

Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.

Metastasis-free survival and patterns of distant metastatic disease after PSMA-PET-guided salvage radiotherapy in recurrent or persistent prostate cancer after prostatectomy

INTRODUCTION

Prostate specific membrane antigen positron-emission tomography (PSMA-PET) is increasingly used to guide salvage radiotherapy (sRT) in prostate cancer (PCa) patients with biochemical recurrence/persistence after prostatectomy. This work examines (i) metastasis-free survival (MFS) following PSMA-PET guided sRT and (ii) the metastatic patterns on PSMA-PET images after sRT.

METHODS

This retrospective, multicenter (9 centers, 5 countries) study included patients referred for PSMA-PET due to recurrent/persistent disease after prostatectomy. Patients with distant metastases (DM) on PSMA-PET prior to sRT were excluded. Cox-regression was performed to assess the impact of clinical parameters on MFS. The distribution of PSMA-PET detected DM following sRT and their respective risk factors were analysed.

RESULTS

All (n=815) patients received intensity-modulated RT to the prostatic fossa. In case of PET-positive pelvic lymph nodes (PLN-PET, n=275, 34%), pelvic lymphatics had been irradiated. Androgen deprivation therapy had been given in 251 (31%) patients. The median follow-up after sRT was 36 months. The 2-/4-year MFS following sRT were 93%/81%. In multivariate analysis the presence of PLN-PET was a strong predictor for MFS (HR=2.39, p<0.001). Following sRT, DM were detected by PSMA-PET in 128/198 (65%) patients and two metastatic patterns were observed: 43% had DM in sub diaphragmatic paraaortic LNs (abdominal-lymphatic) whereas 45% in bones, 9% in supra diaphragmatic LNs and 6% in visceral organs (distant). Two distinct signatures with risk factors for each pattern were identified.

CONCLUSION

MFS in our study is lower compared to previous studies, obviously due to the higher detection rate of DM in PSMA-PET after sRT. Thus, it remains unclear whether MFS is a surrogate endpoint for overall survival in PSMA PET-staged patients in the post sRT setting. PLN-PET may be proposed as a new surrogate parameter predictive of MFS. Analysis of recurrence patterns in PET after sRT revealed risk factor signatures for two metastatic patterns (abdominal-lymphatic and distant), which may allow individualized sRT concepts in the future.

Predictive value of clinical and 18F-FDG-PET/CT derived imaging parameters in patients undergoing neoadjuvant chemoradiation for esophageal squamous cell carcinoma

Aim of this study was to validate the prognostic impact of clinical parameters and baseline ¹⁸F-FDG-PET/CT derived textural features to predict histopathologic response and survival in patients with esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiation (nCRT) and surgery. Between 2005 and 2014, 38 ESCC were treated with nCRT and surgery. For all patients, the ¹⁸F-FDG-PET-derived parameters metabolic tumor volume (MTV), SUVmax, contrast and busyness were calculated for the primary tumor using a SUV-threshold of 3. The parameter uniformity was calculated using contrast-enhanced computed tomography. Based on histopathological response to nCRT, patients were classified as good responders (< 10% residual tumor) (R) or non-responders (≥ 10% residual tumor) (NR). Regression analyses were used to analyse the association of clinical parameters and imaging parameters with treatment response and overall survival (OS). Good response to nCRT was seen in 27 patients (71.1%) and non-response was seen in 11 patients (28.9%). Grading was the only parameter predicting response to nCRT (Odds Ratio (OR) = 0.188, 95% CI: 0.040–0.883; p = 0.034). No association with histopathologic treatment response was seen for any of the evaluated imaging parameters including SUVmax, MTV, busyness, contrast and uniformity. Using multivariate Cox-regression analysis, the heterogeneity parameters busyness (Hazard Ratio (HR) = 1.424, 95% CI: 1.044–1.943; p = 0.026) and contrast (HR = 6.678, 95% CI: 1.969–22.643;p = 0.002) were independently associated with OS, while no independent association with OS was seen for SUVmax and MTV. In patients with ESCC undergoing nCRT and surgery, baseline ¹⁸F-FDG-PET/CT derived parameters could not predict histopathologic response to nCRT. However, the PET/CT derived features busyness and contrast were independently associated with OS and should be further investigated.

Quality of life in patients treated with radiochemotherapy for primary diagnosis of anal cancer

Anal cancer and the related treatment are generally known to affect patients’ quality of life. The aim of this study was to assess self-reported quality of life (QoL) of anal cancer patients after combined radiation and chemotherapy, and to identify patient-, disease-, and therapy-related factors associated with QoL. A total of 94 patients treated with definitive chemoradiation for anal cancer at our institution in the period from 2004 to 2018 were identified from our database. QoL was assessed in the remaining 52 patients using the EORTC QLQ-C30 questionnaire (cancer-specific QoL) and the newly developed anal cancer module QLQ-ANL27 (site-specific QoL). Differences in QoL between anal cancer patients and a German age and sex adjusted reference population were examined. The median follow-up was 71 months (range, 7–176). In the cancer-specific QoL module, the anal cancer cohort presented with significantly lower scores in role (− 12.2 points), emotional (− 6.6 points), and social functioning (− 6.8 points), but higher scores in diarrhea (+ 36.3 points) and constipation (+ 13.3 points) than the German reference population. There were no significant differences in disease- or therapy-related factors, but age greater than 70 years and a follow-up time greater than 71 months had a negative impact on global QoL. As for the site-specific QoL, patients with a tumor relapse showed significantly higher symptom scores than patients with a complete clinical remission in all scales except of micturition frequency. Compared to 3D conformal radiotherapy, IMRT treatment seemed to improve non-stoma bowel function (+ 23.3 points), female sexual functioning (+ 24.2 points), and came along with less scores in the symptom scales pain (− 35.9 points), toilet proximity (− 28.6 points), and cleanliness (− 26.2 points). Most of the functional scores of anal cancer patients were lower compared to the general German population, but did not seem to affect the general QoL. Fatigue, physical, and role functioning had the strongest impact on global QoL causing psychological symptoms as important as physical.

Biomarker signatures for primary radiochemotherapy of locally advanced HNSCC - hypothesis generation on a multicentre cohort of the DKTK-ROG

PURPOSE

To develop prognostic biomarker signatures for patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated by primary radiochemotherapy (RCTx) based on previously published molecular analyses of the retrospective biomarker study of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG).

MATERIAL AND METHODS

In previous studies on the retrospective DKTK-ROG HNSCC cohort treated with primary RCTx, the following clinical parameters and biomarkers were evaluated and found to be significantly associated with loco-regional tumour control (LRC) or overall survival (OS): tumour volume, p16 status, expression of cancer stem cell markers CD44 and SLC3A2, expressions of hypoxia-associated gene signatures, tumour mutational burden (TMB), single nucleotide polymorphisms (SNPs) in the ERCC2 gene (rs1799793, rs13181) and ERCC5 gene (rs17655) as well as the expression of CXCR4, SDF-1 and CD8. These biomarkers were combined in multivariable modelling using Cox-regression with backward variable selection.

RESULTS

A baseline signature containing the widely accepted parameters tumour volume, p16 status, cancer stem cell marker expression (CD44) and hypoxia-associated gene expression has been defined, representing the main hypothesis of the study. Furthermore, the baseline signature was extended by additional prognostic biomarkers and a data-driven signature without any pre-hypothesis was generated for both endpoints. In these signatures, the SNPs rs1799793 and rs17655 as well as CXCR4, SDF-1 and SLC3A2 expression were additionally included. The signatures showed significant patient stratifications for LRC and OS.

CONCLUSION

Three biomarker signatures were defined for patients with locally advanced HNSCC treated with primary RCTx for the endpoints LRC and OS. These signatures will be validated in the prospective HNprädBio study of the DKTK-ROG that recently completed recruitment, before potential application in an interventional trial.

MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy

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-radiomics") 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 <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort.

RESULTS

The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression.

CONCLUSION

This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

BACKGROUND

In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging.

METHODS

Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture.

RESULTS

The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions.

CONCLUSIONS

MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

BACKGROUND

In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS).

METHODS

Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS.

RESULTS

ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification.

CONCLUSIONS

T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Oncological Outcome and Prognostic Factors of Surgery for Soft Tissue Sarcoma After Neoadjuvant or Adjuvant Radiation Therapy: A Retrospective Analysis over 15 Years

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<0.05). Local recurrence was significantly higher in patients with ART (p=0.044).

CONCLUSION

Tumour grade and tumour size were independently associated with disease-specific survival, and patients with local recurrence and metastases had lower survival rates.

Matched-Pair Comparison of 68Ga-PSMA-11 and 18F-rhPSMA-7 PET/CT in Patients with Primary and Biochemical Recurrence of Prostate Cancer: Frequency of Non-Tumor-Related Uptake and Tumor Positivity

Radiohybrid prostate-specific membrane antigen (rhPSMA) ligands are a new class of prostate cancer theranostic agents. ¹⁸F-rhPSMA-7 offers the advantages of ¹⁸F labeling and low urinary excretion compared with ⁶⁸Ga-PSMA-11. Here, we compare the frequency of non-tumor-related uptake and tumor positivity with ⁶⁸Ga-PSMA-11 and ¹⁸F-rhPSMA-7 in patients with primary or recurrent prostate cancer. Methods: This retrospective matched-pair comparison matched 160 ¹⁸F-rhPSMA-7 with 160 ⁶⁸Ga-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 SUV_max of the lesion with the highest PSMA ligand uptake was noted. Tumor positivity rates were determined, and SUV_max was compared separately for each tracer. Results: ¹⁸F-rhPSMA-7 and ⁶⁸Ga-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 ⁶⁸Ga-PSMA-11 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 ⁶⁸Ga-PSMA-11 in both disease stages (113 for ¹⁸F-rhPSMA-7 and 124 for ⁶⁸Ga-PSMA-11). The SUV_max of ¹⁸F-rhPSMA-7 and ⁶⁸Ga-PSMA-11 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 ⁶⁸Ga-PSMA-11 and ¹⁸F-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 ¹⁸F-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 ¹⁸F-rhPSMA-7.

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

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.

Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC

Simple Summary

Radiomic risk models are usually based on imaging features, which are extracted from the entire gross tumour volume (GTVentire). This approach does not explicitly consider the complex biological structure of the tumours. Therefore, in this retrospective study, 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 who were treated with primary radio-chemotherapy. The GTVentire was cropped by different margins to define the rim and corresponding core sub-volumes of the tumour. Furthermore, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. As a result, the models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed an improved performance compared to models based on the corresponding tumour core. This indicates that the consideration of tumour sub-volumes may help to improve radiomic risk models.

Abstract

Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTVentire). 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 GTVentire 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 ± 0.04 (mean ± 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 ± 0.02 and 0.64 ± 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 ± 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.

2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma

For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tumour control (LRC) from treatment-planning computed tomography images. Based on multicentre cohorts for exploration (206 patients) and independent validation (85 patients), multiple deep learning strategies including training of 3D- and 2D-convolutional neural networks (CNN) from scratch, transfer learning and extraction of deep autoencoder features were assessed and compared to a clinical model. Analyses were based on Cox proportional hazards regression and model performances were assessed by the concordance index (C-index) and the model's ability to stratify patients based on predicted hazards of LRC. Among all models, an ensemble of 3D-CNNs achieved the best performance (C-index 0.31) with a significant association to LRC on the independent validation cohort. It performed better than the clinical model including the tumour volume (C-index 0.39). Significant differences in LRC were observed between patient groups at low or high risk of tumour recurrence as predicted by the model ([Formula: see text]). This 3D-CNN ensemble will be further evaluated in a currently ongoing prospective validation study once follow-up is complete.

Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application

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.

Tumor grading of soft tissue sarcomas using MRI-based radiomics

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.

Have we achieved adequate recommendations for target volume definitions in anal cancer? A PET imaging based patterns of failure analysis in the context of established contouring guidelines

Background

There are different contouring guidelines for the clinical target volume (CTV) in anal cancer (AC) which vary concerning recommendations for radiation margins in different anatomical regions, especially on inguinal site. PET imaging has become more important in primary staging of AC as a very sensitive method to detect lymph node (LN) metastases. Using PET imaging, we evaluated patterns of LN spread, and examined the differences of the respective contouring guidelines on the basis of our results.

Methods

We carried out a retrospective study of thirty-seven AC patients treated with chemoradiation (CRT) who underwent FDG-PET imaging for primary staging in our department between 2011 and 2018. Patients showing PET positive LN were included in this analysis. Using a color code, LN metastases of all patients were delineated on a template with “standard anatomy” and were divided indicating whether their location was in- or out-field of the standard CTV as recommended by the Radiation Therapy Oncology Group (RTOG), the Australasian Gastrointestinal Trials Group (AGITG) or the British National Guidance (BNG). Furthermore, a detailed analysis of the location of LN of the inguinal region was performed.

Results

Twenty-two out of thirty-seven AC patients with pre-treatment PET imaging had PET positive LN metastases, accumulating to a total of 154 LN. The most commonly affected anatomical region was inguinal (49 LN, 32%). All para-rectal, external/internal iliac, and pre-sacral LN were covered by the recommended CTVs of the three different guidelines. Of forty-nine involved inguinal LN, fourteen (29%), seven (14%) and five (10%) were situated outside of the recommended CTVs by RTOG, AGITG and BNG. Inguinal LN could be located up to 5.7 cm inferiorly to the femoral saphenous junction and 2.8 cm medial or laterally to the big femoral vessels.

Conclusion

Pelvis-related, various recommendations are largely consistent, and all LN are covered by the recommended CTVs. LN “misses” appear generally cranially (common iliac or para-aortic) or caudally (inguinal) to the recommended CTVs. The established guidelines differ significantly, particular regarding the inguinal region. Based on our results, we presented our suggestions for CTV definition of the inguinal region. LN involvement of a larger number of patients should be investigated to enable final recommendations.

MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma

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.

Neoadjuvant image-guided helical intensity modulated radiotherapy of extremity sarcomas - a single center experience

Background

Advanced radiotherapy (RT) techniques allow normal tissue to be spared in patients with extremity soft tissue sarcoma (STS). This work aims to evaluate toxicity and outcome after neoadjuvant image-guided radiotherapy (IGRT) as helical intensity modulated radiotherapy (IMRT) with reduced margins based on MRI-based target definition in patients with STS.

Methods

Between 2010 to 2014, 41 patients with extremity STS were treated with IGRT delivered as helical IMRT on a tomotherapy machine. The tumor site was in the upper extremity in 6 patients (15%) and lower extremity in 35 patients (85%). Reduced margins of 2.5 cm in longitudinal direction and 1.0 cm in axial direction were used to expand the MRI-defined gross tumor volume, including peritumoral edema, to the clinical target volume. An additional margin of 5 mm was added to receive the planning target volume. The full total dose of 50 Gy in 2 Gy fractions was sucessfully applied in 40 patients. Two patients received chemotherapy instead of surgery due to systemic progression. All patients were included into a strict follow-up program and were seen interdisciplinarily by the Departments of Orthopaedic Surgery and Radiation Oncology.

Results

Thirty eight patients that received total RT total dose and subsequent resection were analyzed for outcome. After a median follow-up of 38.5 months cumulative OS, local PFS and systemic PFS at 2 years were determined at 78.2, 85.2 and 54.5%, respectively. Two of 6 local recurrences were proximal marginal misses. Negative resection margins were achieved in 84% of patients. The rate of major wound complications was comparable to previous IMRT studies with 36.8%. RT was overall tolerable with low toxicity rates.

Conclusions

IMRT-IGRT offers neoadjuvant treatment for extremity STS with reduced safety margins and thus low toxicity rates. Wound complication rates were comparable to previously reported frequencies. Two reported marginal misses suggest a word of caution for reduction of longitudinal safety margins.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1207-2) contains supplementary material, which is available to authorized users.

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme

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.

Impact of VMAT-IMRT compared to 3D conformal radiotherapy on anal sphincter dose distribution in neoadjuvant chemoradiation of rectal cancer

Background

Neoadjuvant radio- or chemoradiation (nIRT) therapy is the standard treatment for loco-regional advanced rectal cancer patients of the lower or middle third. Currently, intensity modulated radiation therapy (IMRT) is not the recommended radiation technique even though IMRT has advantages compared to 3D-radiation regarding dose sparing to organs at risk like small bowel and urinary bladder. So far, the benefit of IMRT concerning the anal sphincter complex is not examined. With this study we intended to evaluate the dose distribution on the anal sphincters of rectal cancer patients treated with IMRT in comparison with 3D-techniques.

Methods

We selected 16 patients for the IMRT-group and 16 patients for the 3D-group with rectal cancer of the middle third who were treated in our institute. All patients received 45 Gy in a chemoradiation protocol. Patients in both groups were matched regarding stage, primary tumor distance to the anal verge and size of the tumor. We delineated the internal and external anal sphincters, the addition of both sphincters and the levator ani muscle in all patients. Subsequently, we evaluated and compared dose parameters of the different sphincters in both groups and analysed the configuration of the isodoses in the area of the caudal radiation field, respectively.

Results

Most of the relevant dose parameters of the caudal sphincters (Dmean, Dmedian, V10–V40) were significantly reduced in the IMRT-group compared to the 3D-group. Accordingly, the isodoses at the caudal edge of the target volume in the IMRT group demonstrated a steep dose fall. The levator ani muscle always was included into the planned target volumes and received the full dose in both groups.

Conclusions

The modern VMAT-IMRT can significantly reduce the dose to the anal sphincters for rectal cancer patients of the middle third who were treated with conventional chemoradiation therapy.

Dosimetric comparison of different radiation techniques (IMRT vs. 3-dimensional) of the "true" (deep) ano-inguinal lymphatic drainage of anal cancer patients

Introduction

The ano-inguinal lymphatic drainage (AILD) is located in the subcutaneous adipose tissue of the proximal medial thigh. Currently, there are no recommendations for an inclusion of the ‘true’ AILD in the clinical target volume (CTV) of definitive chemoradiation for anal cancer patients. To estimate the relevance of inguinal recurrence, we compared the incidental dose to the AILD in anal cancer (AC) patients who were treated either with Volumetric Arc Therapy – Intensity Modulated Radiation Therapy (VMAT-IMRT) or conventional 3D-radiation technique.

Methods

One VMAT-IMRT-plans and one 3D-plans were calculated on the same target volumes and identical dose prescription in ten patients. We defined the volume of the AILD on the planning CT-scans based on the information of new fluorescence methods. Furthermore, we defined several anatomical subvolumes of interest inside the AILD. We examined and compared absolute and relative dosimetric parameters of the AILD and different anatomical subunits.

Results

The Dmean of the AILD was 40 Gy in the 3D-group and 38 Gy in the IMRT-group. Dmean and Dmedian as well as the V30Gy of the AILD and all subvolumes of the caudal AILD were significant higher using 3D-RT compared to IMRT. Even though the absolute differences were small, in the caudal aspect of the ano-inguinal lymphatic drainage the V30Gy could be more than 10% less with VMAT-IMRT.

Conclusions

3D-RT was slightly superior to IMRT in terms of dose coverage of the AILD. However, the absolute differences were very small. Some relevant caudal parts of the AILD received an insufficient dose for treating potential micrometastases. Particularly in high-risk situations, this may lead to inguinal recurrence and therefore the true deep AILD should be included into the target volume in high risk patients.

Epigenetic regulation of NFE2 overexpression in myeloproliferative neoplasms

The transcription factor "nuclear factor erythroid 2" (NFE2) is overexpressed in the majority of patients with myeloproliferative neoplasms (MPNs). In murine models, elevated NFE2 levels cause an MPN phenotype with spontaneous leukemic transformation. However, both the molecular mechanisms leading to NFE2 overexpression and its downstream targets remain incompletely understood. Here, we show that the histone demethylase JMJD1C constitutes a novel NFE2 target gene. JMJD1C levels are significantly elevated in polycythemia vera (PV) and primary myelofibrosis patients; concomitantly, global H3K9me1 and H3K9me2 levels are significantly decreased. JMJD1C binding to the NFE2 promoter is increased in PV patients, decreasing both H3K9me2 levels and binding of the repressive heterochromatin protein-1α (HP1α). Hence, JMJD1C and NFE2 participate in a novel autoregulatory loop. Depleting JMJD1C expression significantly reduced cytokine-independent growth of an MPN cell line. Independently, NFE2 is regulated through the epigenetic JAK2 pathway by phosphorylation of H3Y41. This likewise inhibits HP1α binding. Treatment with decitabine lowered H3Y41ph and augmented H3K9me2 levels at the NFE2 locus in HEL cells, thereby increasing HP1α binding, which normalized NFE2 expression selectively in JAK2V617F-positive cell lines.

Radiomics in radiooncology - Challenging the medical physicist

PURPOSE

Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions.

METHODS

Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach.

RESULTS

Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team.

CONCLUSIONS

The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.