Cancer Radiotherapie最新文献

Purpose

Cranial irradiation can lead to long-term neurological complications, in particular memory disorders. The aim of this prospective study is to evaluate the impact of irradiation of benign skull base tumours located near the hippocampi on autobiographical memory.

Patients and methods

From 2016 to 2019, patients with cavernous sinus meningioma or pituitary adenoma treated with normofractionated irradiation were included. Patients underwent full neuropsychological assessment at baseline, 1 year and 2 years post-treatment. Neuropsychological tests were converted to Z-Score for comparability.

Results

Twelve of the 19 patients included had a complete neuropsychological evaluation at 2 years and were analysed. On the “TEMPau” test, no significant difference in autobiographical memory was found at 2 years, regardless of the period of autobiographical memory. The mean hippocampal dose had no impact on the variation in autobiographical memory. There was no significant cognitive impairment in the other domains assessed, such as attention, anterograde memory, working memory and executive functions. Autobiographical memory was independent of these other cognitive domains, which justifies its specific study.

Conclusion

Radiotherapy to the skull base for a benign pathology does not lead to significant cognitive impairment. Longer follow-up would be needed to confirm these results.

Nuclear protein in testis (NUT) carcinoma is a rare neoplasm arising mainly from midline structures. It is an aggressive type of carcinoma associated with poor survival despite the use of multiple treatment modalities. Here, we present a case of a 17-year-old paediatric patient with NUT carcinoma of larynx, which is even rarer among all reported cases. The patient underwent surgery followed by radiotherapy and systemic treatment and he died 15 months after the diagnosis. The management of this rare disease requires further investigation.

Purpose

Many series have compared voice quality after radiotherapy or surgery for cT1 glottic carcinoma. Different meta-analyses identify better results for radiotherapy while others do not identify any difference, some finally find a superiority of surgery. The purpose of this study was to compare the voice quality in the long term of patients who underwent transoral surgery versus exclusive irradiation for the treatment of cT1 glottic carcinoma.

Material and methods

The VOQUAL study was a pilot comparative multicenter cross-sectional study. The primary endpoint was the Voice Handicap Index comparison between two groups (radiotherapy or surgery). The voice assessment also consisted in the heteroevaluation of voice quality by the Grade, Roughness, Breathness, Asthenia, and Strain rating scale reported by Hirano.

Results

The study included 41 adult patients with cT1 carcinoma of the vocal cord treated by cordectomy or exclusive radiation in two oncologic centers. The median Voice Handicap Index value was 20 [8; 32.5] in the surgery group and 10 [4; 18.5] in the radiotherapy group. There was no statistically significant difference between the median values and the various components F, P and E of the questionnaire (P = 0.1585). The median value of the numeric dysphonia Grade, Roughness, Breathness, Asthenia, and Strain scale was 2 [0; 5] in the surgery group and 2 [0.25; 3.75] in the radiotherapy group. There was no statistically significant difference between these values (P = 0.78).

Conclusion

Our study did not show any significant difference on the primary endpoints of Voice Handicap Index and Grade, Roughness, Breathness, Asthenia, and Strain scores.

Level of evidence

III. Clinical trial registration: The VOQUAL study was registered on the ClinicalTrials.gov platform under the number NCT04447456, in July 2020.

Purpose

This study aimed to design an autodelineation model based on convolutional neural networks for generating high-risk clinical target volumes and organs at risk in image-guided adaptive brachytherapy for cervical cancer.

Materials and methods

A novel SERes-u-net was trained and tested using CT scans from 98 patients with locally advanced cervical cancer who underwent image-guided adaptive brachytherapy. The Dice similarity coefficient, 95th percentile Hausdorff distance, and clinical assessment were used for evaluation.

Results

The mean Dice similarity coefficients of our model were 80.8%, 91.9%, 85.2%, 60.4%, and 82.8% for the high-risk clinical target volumes, bladder, rectum, sigmoid, and bowel loops, respectively. The corresponding 95th percentile Hausdorff distances were 5.23 mm, 4.75 mm, 4.06 mm, 30.0 mm, and 20.5 mm. The evaluation results revealed that 99.3% of the convolutional neural networks-generated high-risk clinical target volumes slices were acceptable for oncologist A and 100% for oncologist B. Most segmentations of the organs at risk were clinically acceptable, except for the 25% sigmoid, which required significant revision in the opinion of oncologist A. There was a significant difference in the clinical evaluation of convolutional neural networks-generated high-risk clinical target volumes between the two oncologists (P < 0.001), whereas the score differences of the organs at risk were not significant between the two oncologists. In the consistency evaluation, a large discrepancy was observed between senior and junior clinicians. About 40% of SERes-u-net-generated contours were thought to be better by junior clinicians.

Conclusion

The high-risk clinical target volumes and organs at risk of cervical cancer generated by the proposed convolutional neural networks model can be used clinically, potentially improving segmentation consistency and efficiency of contouring in image-guided adaptive brachytherapy workflow.

Purpose

The purpose of this study was to assess if multicriteria optimization could limit interoperator variability in radiation therapy planning and assess if this method could contribute to target volume coverage and sparing of organ at risk for intensity-modulated curative radiation therapy of head and neck cancers.

Material and methods

We performed a retrospective analysis on 20 patients treated for an oropharyngeal or oral cavity squamous cell carcinoma. We carried out a comparative dosimetric study of manual plans produced with Precision® software, compared with the plans proposed using the multicriteria optimization method (RayStation®). We assessed interoperator reproducibility on the first six patients, and dosimetric contribution in sparing organs at risk using the multicriteria optimization method.

Results

Median age was 69 years, most lesions were oropharyngeal carcinoma (65%), and 35% lesions were stage T3. First, we obtained a high degree of similarity between the four operator measurements for each patient at the level of each organ. Intraclass correlation coefficients were greater than 0.85. Second, we observed a significant dosimetric benefit for contralateral parotid gland, homolateral and contralateral masseter muscles, homolateral and contralateral pterygoid muscles and for the larynx (P < 0.05). For the contralateral parotid gland, the mean dose difference between the multicriteria optimization and manual plans was −2.0 Gy (P = 0.01). Regarding the larynx, the mean dose difference between the two plans was −4.6 Gy (P < 0.001).

Conclusion

Multicriteria optimization is a reproducible technique and faster than manual optimization. It allows dosimetric advantages on organs at risk, especially for those not usually taken into consideration in manual dosimetry. This may lead to improved quality of life.

Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy.

Neoadjuvant chemoradiotherapy is the standard treatment for patients with locally advanced rectal cancers owing to its ability to downstage primary tumours. Some patients can achieve pathological complete response after neoadjuvant therapy, and can adopt a “watch and wait” treatment strategy to avoid overtreatment. Therefore, it is essential to develop strategies for predicting responses to neoadjuvant therapy. Radiomics has shown great potential in extracting tumour features from high-throughput medical images for the construction of mathematics models for predicting the effects of anticancerous therapies. Herein, we explored MRI-based radiomics and found that it can predict responses of locally advanced rectal cancers to chemoradiation. Efficient radiomics model allow early-stage prediction of the effect of neoadjuvant chemoradiotherapy on locally advanced rectal cancers. It helps clinicians to make informed therapeutic decisions. In this review, we discuss the workflow of radiomics, and summarize the clinical application of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer.

Purpose

Neurocytomas represent 0.25 to 0.5% of primary brain tumours and are mainly found in young adults. These tumours have neuronal differentiation. The cornerstone treatment is neurosurgery. The efficacy of other therapies, including radiotherapy, is still unclear. The objective of this study was to evaluate the management of central neurocytomas and the role of radiotherapy.

Materials and methods

All adult patients (age 18 years or older) newly diagnosed with a histologically confirmed neurocytoma between 2006 and 2015 in France were included.

Results

One hundred and sixteen patients were diagnosed with a central neurocytoma during the study period. All patients underwent surgical resection, and six received adjuvant radiotherapy. Eleven patients received radiotherapy due to progression. After a median follow-up of 68.7 months, local failure occurred in 29 patients. The 5-year local control rate was 73.4%. According to univariate analysis, marker of proliferation Ki67 index greater than 2% (hazard ratio [HR]: 1.48; confidence interval [CI]: 1.40–1.57; P = 0.027) and subtotal resection (HR: 8.48; CI: 8.01–8.99; P < 0.001) were associated with an increase in local failure. Gross total resection was associated with a higher risk of sequelae epilepsy (HR: 3.62; CI: 3.42–3.83; P < 0.01) and memory disorders (HR: 1.35; CI: 1.07–1.20; P < 0.01). Ten patients (8.6%) died during the follow-up. The 10-year overall survival rate was 89.0%. No prognostic factors for overall survival were found.

Conclusion

The analysis showed that patients who underwent subtotal surgical resection, particularly when the tumour had a Ki67 index greater than 2%, had an increased risk of local recurrence. These patients could benefit from adjuvant radiotherapy.