Different studies have proved in recent years that hypofractionated radiotherapy (RT) improves overall survival of patients affected by locally advanced, unresectable, pancreatic cancer. The clinical ...management of these patients generally leads to poor results and is considered very challenging, due to different factors, heavily influencing treatment delivery and its outcomes. Firstly, the dose prescribed to the target is limited by the toxicity that the highly radio-sensitive organs at risk (OARs) surrounding the disease can develop. Treatment delivery is also complicated by the significant inter-fractional and intra-fractional variability of therapy volumes, mainly related to the presence of hollow organs and to the breathing cycle. The recent introduction of magnetic resonance guided radiotherapy (MRgRT) systems leads to the opportunity to control most of the aforementioned sources of uncertainty influencing RT treatment workflow in pancreatic cancer. MRgRT offers the possibility to accurately identify radiotherapy volumes, thanks to the high soft-tissue contrast provided by the Magnetic Resonance imaging (MRI), and to monitor the tumour and OARs positions during the treatment fraction using a high-temporal cine MRI. However, the main advantage offered by the MRgRT is the possibility to online adapt the RT treatment plan, changing the dose distribution while the patient is still on couch and successfully addressing most of the sources of variability.
Aim of this study is to present and discuss the state of the art, the main pitfalls and the innovative opportunities offered by online adaptive MRgRT in pancreatic cancer treatment.
Purpose
Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients ...using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner.
Methods
In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with
Z
-score normalisation and an initial feature selection was carried out using Wilcoxon–Mann–Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness.
Results
Three features were selected: maximum fractal dimension with IB = 0–50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0–50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively.
Conclusions
The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.
Purpose
Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced ...disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).
Materials and methods
This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC).
Results
The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (
p
= 0.6) between yMTB and CM.
Conclusion
Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.
•We propose a deep learning approach to generate synthetic CT from low tesla MR images.•Synthetic CT was created for pelvic and abdominal lesions enrolling 120 cases.•The images generated show an ...accuracy sufficient to safely calculate IMRT plans.•This study represents the first experience of CT generation from low tesla MR images.•This is the first experience of CT generation in abdomen independently by B intensity.
Artificial intelligence (AI) can play a significant role in Magnetic Resonance guided Radiotherapy (MRgRT), especially to speed up the online adaptive workflow. The aim of this study is to set up a Deep Learning (DL) approach able to generate synthetic computed tomography (sCT) images from low field MR images in pelvis and abdomen.
A conditional Generative Adversarial Network (cGAN) was used for sCT generation: a total of 120 patients treated on pelvic and abdominal sites were enrolled and divided in training (80) and test sets (40).
Intensity modulated radiotherapy (IMRT) treatment plans were calculated on sCT and original CT and then compared in terms of gamma analysis and differences in Dose Volume Histogram (DVH).
The two one-sided test for paired samples (TOST-P) was used to evaluate the equivalence among different DVH parameters calculated for target and organs at risks (OAR) on CT and sCT images.
Using a CPU architecture, the mean time required by the neural network to generate a synthetic CT was 175 ± 43 seconds (s) for pelvic cases and 110 ± 40 s for abdominal ones.
Mean gamma passing rates for the three tolerance criteria analysed (1%/1 mm, 2%/2 mm and 3%/3 mm) were respectively 90.8 ± 4.5%, 98.7 ± 1.1% and 99.8 ± 0.2% for abdominal cases; 89.3 ± 4.8%, 99.0 ± 0.7% and 99.9 ± 0.2% for pelvic ones, while equivalence within 1% was observed among the DVH indicators.
This study demonstrated that sCT generation using a DL approach is feasible for low field MR images in pelvis and abdomen, allowing a reliable calculation of IMRT plans in MRgRT.
Purpose
The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally ...advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT).
Materials and methods
We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions.
Radiomics features were extracted from pre-treatment 1.5 T T2w MR images.
The predictive performance of each feature was quantified in terms of Wilcoxon–Mann–Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value.
The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC).
Results
A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one).
1896 radiomic feature were extracted, 91 of which showed significance (
p
< 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set.
Conclusions
The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.
Distant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation ...of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonance imaging (MRI) with 2 years distant metastasis (2yDM) rate in LARC patients.
Diagnostic pre- and post- nCRT MRI of LARC patients, treated in a single institution from May 2008 to June 2015 with an adequate follow-up time, were retrospectively collected. Gross tumor volumes (GTV) were contoured by an abdominal radiologist and blindly reviewed by a radiation oncologist expert in rectal cancer. The dataset was firstly randomly split into 90% training data, for features selection, and 10% testing data, for the validation. The final set of features after the selection was used to train 15 different classifiers using accuracy as target metric. The models' performance was then assessed on the testing data and the best performing classifier was then selected, maximising the confusion matrix balanced accuracy (BA).
Data regarding 213 LARC patients (36% female, 64% male) were collected. Overall 2yDM was 17%. A total of 2,606 features extracted from the pre- and post- nCRT GTV were tested and 4 features were selected after features selection process. Among the 15 tested classifiers, logistic regression proved to be the best performing one with a testing set BA, sensitivity and specificity of 78.5%, 71.4% and 85.7%, respectively.
This study supports a possible role of delta radiomics in predicting following occurrence of distant metastasis. Further studies including a consistent external validation are needed to confirm these results and allows to translate radiomics model in clinical practice. Future integration with clinical and molecular data will be mandatory to fully personalized treatment and follow-up approaches.
The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in ...locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario “Agostino Gemelli” of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.
Purpose
Hepatocellular carcinoma (HCC) in early stages benefits from local ablative treatments such as radiofrequency ablation (RFA) or transarterial chemoembolization (TACE). In this context, ...radiotherapy (RT) has shown promising results but has not been thoroughly evaluated. Magnetic resonance-guided RT (MRgRT) may represent a paradigm shifting improvement in stereotactic body radiotherapy (SBRT) for liver tumors.
Methods
We retrospectively evaluated HCC patients treated on a hybrid low-tesla MRgRT unit. A total biologically effective dose (BED) > 100 Gy was delivered in 5 consecutive fractions, respecting the appropriate organs-at-risk constraints. Hybrid MR scans were used for treatment planning and cine MR was used for delivery gating. Patients were followed up for toxicity and treatment–response assessment.
Results
Ten patients were enrolled, with a total of 12 lesions. All the lesions were irradiated with no interruptions. Six patients had already performed previous local therapies. Median follow-up after SBRT was 6.5 months (1–25). Two cases of acute toxicity were reported (G ≤ 2 according to CTCAE v4.0). At the time of the analysis, 90% of the population presented local control. Child–Pugh before and after treatment remained unchanged in all but one patient.
Conclusion
MRgRT is a feasible and safe option showing favorable toxicity profile for HCC treatment.
Neoadjuvant chemoradiation therapy (nCRT) is the standard treatment modality in locally advanced rectal cancer (LARC). Since response to radiotherapy (RT) is dose dependent in rectal cancer, dose ...escalation may lead to higher complete response rates. The possibility to predict patients who will achieve complete response (CR) is fundamental. Recently, an early tumour regression index (ERI) was introduced to predict pathological CR (pCR) after nCRT in LARC patients. The primary endpoints will be the increase of CR rate and the evaluation of feasibility of delta radiomics-based predictive MRI guided Radiotherapy (MRgRT) model.
Patients affected by LARC cT2-3, N0-2 or cT4 for anal sphincter involvement N0-2a, M0 without high risk features will be enrolled in the trial. Neoadjuvant CRT will be administered using MRgRT. The initial RT treatment will consist in delivering 55 Gy in 25 fractions on Gross Tumor Volume (GTV) plus the corresponding mesorectum and 45 Gy in 25 fractions on the drainage nodes. Chemotherapy with 5-fluoracil (5-FU) or oral capecitabine will be administered continuously. A 0.35 Tesla MRI will be acquired at simulation and every day during MRgRT. At fraction 10, ERI will be calculated: if ERI will be inferior than 13.1, the patient will continue the original treatment; if ERI will be higher than 13.1 the treatment plan will be reoptimized, intensifying the dose to the residual tumor at the 11
fraction to reach 60.1 Gy. At the end of nCRT instrumental examinations are to be performed in order to restage patients. In case of stable disease or progression, the patient will undergo surgery. In case of major or complete clinical response, conservative approaches may be chosen. Patients will be followed up to evaluate toxicity and quality of life. The number of cases to be enrolled will be 63: all the patients will be treated at Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome.
This clinical trial investigates the impact of RT dose escalation in poor responder LARC patients identified using ERI, with the aim of increasing the probability of CR and consequently an organ preservation benefit in this group of patients.
ClinicalTrials.gov Identifier: NCT04815694 (25/03/2021).
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