Radiation Oncology最新文献

Purpose: To evaluate the relationship between delivered radiation (RT) and post-RT inversion-recovery ultrashort-echo-time (IR-UTE) MRI signal-intensity (SI) in gynecologic cancer patients treated with high-dose-rate (HDR) brachytherapy (BT).

Methods: Seven patients underwent whole-pelvis RT (WPRT) followed by BT to the high-risk clinical target volume (HR-CTV). MR images were acquired at three time-points; pre-RT, post-WPRT/pre-BT, and 3-6 months post-BT. Diffuse-fibrosis (F_Diffuse) was imaged with a non-contrast dual-echo IR (inversion time [TI] = 60 ms) UTE research application, with image-subtraction of the later echo, only retaining the ultrashort-echo SI. Dense-fibrosis (F_Dense) imaging utilized single-echo Late-Gadolinium-Enhanced IR-UTE, acquired ∼ 15 min post-Gadavist injection. Resulting F_Diffuse and F_Dense SI were normalized to the corresponding gluteal-muscle SI. Images were deformably registered between time-points based on normal tissue anatomy. The remnant tumor at both time-points was segmented using multi-parametric MRI. Contours corresponding to the 50%, 100%, 150%, and 200% isodose lines (IDLs) of the prescription BT-dose were created. Mean F_Diffuse and F_Dense SI within (i) each IDL contour and (ii) the remnant tumor were calculated. Post-BT F_Diffuse and F_Dense SI were correlated with prescribed BT-dose. To determine the relationship between BT-dose and IR-UTE SI, the differences in the post-BT F_Dense across IDLs was determined using paired t-tests with Bonferroni correction.

Results: F_Dense was higher in regions of higher dose for 6/7 patients, with mean ± SD values of 357 ± 103% and 331 ± 97% (p = .03) in the 100% and 50% IDL, respectively. F_Dense was higher in regions of higher dose in the responsive regions with mean ± SD values of 380 ± 122% and 356 ± 135% (p = .03) in the 150% and 50% IDL, respectively. Within the segmented remnant tumor, an increase in prescribed dose correlated with an increase in F_Dense post-BT (n = 5, r = .89, p = .04). Post-BT F_Diffuse inversely correlated (n = 7, r = -.83, p = .02) with prescribed BT-dose within the 100% IDL.

Conclusions: Results suggest that F_Dense SI 3-6 months post-BT is a sensitive measure of tissue response to heterogeneous BT radiation-dose. Future studies will validate whether F_Diffuse and F_Dense are accurate biomarkers of fibrotic radiation response.

Breast conserving treatment typically involves surgical excision of tumor and adjuvant radiotherapy targeting the breast area or tumor bed. Accurately defining the tumor bed is challenging and lead to irradiation of greater volume of healthy tissues. Preoperative stereotactic body radiotherapy (SBRT) which target tumor may solves that issues. We conducted a systematic literature review to evaluates the early toxicity and cosmetic outcomes of this promising treatment approach. Secondary we reviewed pathological complete response (pCR) rates, late toxicity, patient selection criteria and radiotherapy protocols. We retrieved literature from PubMed, Scopus, Web of Science, Cochrane, ScienceDirect, and ClinicalTrials.gov. The study adhered to the PRISMA 2020 guidelines. Ten prospective clinical trials (7 phase II, 3 phase I), encompassing 188 patients (aged 18-75 years, cT1-T3 cN0-N3 cM0, primarily with ER/PgR-positive, HER2-negative status,), were analyzed. Median follow-up was 15 months (range 3-30). Treatment involved single-fraction SBRT (15-21Gy) in five studies and fractionated (19.5-31.5Gy in 3 fractions) in the rest. Time interval from SBRT to surgery was 9.5 weeks (range 1-28). Acute and late G2 toxicity occurred in 0-17% and 0-19% of patients, respectively, G3 toxicity was rarely observed. The cosmetic outcome was excellent in 85-100%, fair in 0-10% and poor in only 1 patient. pCR varied, showing higher rates (up to 42%) with longer intervals between SBRT and surgery and when combined with neoadjuvant systemic therapy (up to 90%). Preoperative SBRT significantly reduce overall treatment time, enabling to minimalize volumes. Early results indicate excellent cosmetic effects and low toxicity.

Background: Radiotherapy (RT) in head and neck squamous cell cancer (HNSCC) often leads to sticky saliva and xerostomia (SSX). Dose sparing of salivary glands (SG) reduces occurrence of SSX but few studies investigated the relationship between RT dose to SG substructures and SSX. We therefore investigated this hypothesis, focusing on the parotid duct (PD).

Methods: Retrospective data was collected from 99 HNSCC patients treated at our center with (chemo-)radiotherapy (CRT). PD and other organs-at-risk (OAR) were (re-)contoured and DVHs were generated without re-planning. SSX was graded according to CTCAE v.4.03 and evaluated at acute, subacute, and two late timepoints.

Results: Most patients presented with loco-regionally advanced disease. In 47% of patients, up-front neck dissection preceded CRT. Weighted mean dose was 28.6 Gy for bilateral parotid glands (PG), and 32.0 Gy for PD. Acute SSX presented as grades 0 (35.3%), I (41.4%), II (21.2%) and III (2.0%). There was no association of OARs and SSX ≥ grade 2 in univariable logistic regression (LR). Multivariable LR showed statistically significant relationship of acute SSX with: PG weighted mean dose (OR 0.84, p = 0.004), contralateral PG mean dose (OR 1.14, p = 0.02) and contralateral PD planning OAR (PD PRV) mean dose (OR 1.84, p = 0.03).

Conclusions: There was an association of acute SSX with dose exposure of PD PRV in multivariable regression, only. Due to statistical uncertainties and the retrospective nature of this analysis, further studies are required to confirm or reject the hypothesis.

Background: Pathological complete response (pCR) is a well-established prognostic factor in breast cancer treated with neoadjuvant systemic therapy (naST). The determining factors of pCR are known to be intrinsic subtype, proliferation index, grading, clinical tumor and nodal stage as well as type of systemic therapy. The addition of neoadjuvant radiotherapy (naRT) to this paradigm might improve response, freedom from disease, toxicity and cosmetic outcome compared to adjuvant radiotherapy. The factors for pCR and primary tumor regression when neoadjuvant radiation therapy is added to chemotherapy have not been thoroughly described.

Methods: We performed a retrospective analysis of 341 patients (cT1-cT4/cN0-N+) treated with naRT and naST between 1990 and 2003. Patients underwent naRT to the breast and mostly to the supra-/infraclavicular lymph nodes combined with an electron or brachytherapy boost. NaST was given either sequentially or simultaneously to naRT using different regimens. We used the univariate and multivariate regression analysis to estimate the effect of different subgroups and treatment modalities on pCR (ypT0/Tis and ypN0) as well as complete primary tumor response (ypT0/Tis; bpCR) in our cohort. Receiver operating characteristic (ROC) analysis was performed to evaluate the interval between radiotherapy (RT) and resection (Rx) as well as radiotherapy dose.

Results: Out of 341 patients, pCR and pbCR were achieved in 31% and 39%, respectively. pCR rate was influenced by resection type, breast cancer subtype, primary tumor stage and interval from radiation to surgery in the multivariate analysis. Univariate analysis of bpCR showed age, resection type, breast cancer subtype, clinical tumor stage and grading as significant factors. Resection type, subtype and clinical tumor stage remained significant in multivariate analysis. Radiation dose to the tumor and interval from radiation to surgery were not significant factors for pCR. However, when treatment factors were added to the model, a longer interval from radiotherapy to resection was a significant predictor for pCR.

Conclusions: The factors associated with pCR following naST and naRT are similar to known factors after naST alone. Longer interval to surgery might to be associated with higher pCR rates. Dose escalation beyond 60 Gy did not result in higher response rates.

Background: The purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique.

Methods: A total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models.

Results: The models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models.

Conclusions: Machine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans.

Trial registration: This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276.

Background: PSMA-PET is increasingly used for staging prostate cancer (PCA) patients. However, it is not clear if quantitative imaging parameters of positron emission tomography (PET) have an impact on disease progression and are thus important for the prognosis of localized PCA.

Methods: This is a monocenter retrospective analysis of 86 consecutive patients with localized intermediate or high-risk PCA and PSMA-PET before treatment The quantitative PET parameters maximum standardized uptake value (SUV_max), tumor asphericity (ASP), PSMA tumor volume (PSMA-TV), and PSMA total lesion uptake (PSMA-TLU = PSMA-TV × SUV_mean) were assessed for their prognostic significance in patients with radiotherapy or surgery. Cox regression analyses were performed for biochemical recurrence-free survival, overall survival (OS), local control, and loco-regional control (LRC).

Results: 67% of patients had high-risk disease, 51 patients were treated with radiotherapy, and 35 with surgery. Analysis of metric PET parameters in the whole cohort revealed a significant association of PSMA-TV (p = 0.003), PSMA-TLU (p = 0.004), and ASP (p < 0.001) with OS. Upon binarization of PET parameters, several other parameters showed a significant association with clinical outcome. When analyzing high-risk patients according to the primary treatment approach, a previously published cut-off for SUV_max (8.6) showed a significant association with LRC in surgically treated (p = 0.048), but not in primary irradiated (p = 0.34) patients. In addition, PSMA-TLU (p = 0.016) seemed to be a very promising biomarker to stratify surgical patients.

Conclusion: Our data confirm one previous publication on the prognostic impact of SUV_max in surgically treated patients with high-risk PCA. Our exploratory analysis indicates that PSMA-TLU might be even better suited. The missing association with primary irradiated patients needs prospective validation with a larger sample size to conclude a predictive potential. Trial registration Due to the retrospective nature of this research, no registration was carried out.

Background: In this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa).

Methods: Multiple deep learning CNN architectures were developed using the Unet as the baseline. The CNNs use both multiparametric MRI images (T2W, ADC, and High b-value) and quantitative clinical data (prostate specific antigen (PSA), PSA density (PSAD), prostate gland volume & gross tumor volume (GTV)), and only mp-MRI images (n = 118), as input. In addition, co-registered ground truth data from whole mount histopathology images (n = 22) were used as a test set for evaluation.

Results: The CNNs achieved for early/intermediate / late level fusion a precision of 0.41/0.51/0.61, recall value of 0.18/0.22/0.25, an average precision of 0.13 / 0.19 / 0.27, and F scores of 0.55/0.67/ 0.76. Dice Sorensen Coefficient (DSC) was used to evaluate the influence of combining mpMRI with parametric clinical data for the detection of csPCa. We compared the DSC between the predictions of CNN's trained with mpMRI and parametric clinical and the CNN's trained with only mpMRI images as input with the ground truth. We obtained a DSC of data 0.30/0.34/0.36 and 0.26/0.33/0.34 respectively. Additionally, we evaluated the influence of each mpMRI input channel for the task of csPCa detection and obtained a DSC of 0.14 / 0.25 / 0.28.

Conclusion: The results show that the decision level fusion network performs better for the task of prostate lesion detection. Combining mpMRI data with quantitative clinical data does not show significant differences between these networks (p = 0.26/0.62/0.85). The results show that CNNs trained with all mpMRI data outperform CNNs with less input channels which is consistent with current clinical protocols where the same input is used for PI-RADS lesion scoring.

Trial registration: The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under proposal number Nr. 476/14 & 476/19.