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Purpose: Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a “watch and wait” strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging.
Experimental design: We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial.
Results: A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76).
Conclusion: The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a “watch and wait” strategy.
Translational relevance: Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for “watch and wait”.
Purpose: Scientific and clinical achievements in radiation, medical, and surgical oncology are changing the landscape of interdisciplinary oncology. The German Society for Radiation Oncology (DEGRO) working group of young clinicians and scientists (yDEGRO) and the DEGRO representation of associate and full professors (AKRO) are aware of the essential role of radiation oncology in multidisciplinary treatment approaches. Together, yDEGRO and AKRO endorsed developing a German radiotherapy & radiation oncology vision 2030 to address future challenges in patient care, research, and education. The vision 2030 aims to identify priorities and goals for the next decade in the field of radiation oncology. Methods: The vision development comprised three phases. During the first phase, areas of interest, objectives, and the process of vision development were defined jointly by the yDEGRO, AKRO, and the DEGRO board. In the second phase, a one-day strategy retreat was held to develop AKRO and yDEGRO representatives’ final vision from medicine, biology, and physics. The third phase was dedicated to vision interpretation and program development by yDEGRO representatives. Results: The strategy retreat’s development process resulted in conception of the final vision “Innovative radiation oncology Together – Precise, Personalized, Human.” The first term “Innovative radiation oncology” comprises the promotion of preclinical research and clinical trials and highlights the development of a national committee for strategic development in radiation oncology research. The term “together” underpins collaborations within radiation oncology departments as well as with other partners in the clinical and scientific setting. “Precise” mainly covers technological precision in radiotherapy as well as targeted oncologic therapeutics. “Personalized” emphasizes biology-directed individualization of radiation treatment. Finally, “Human” underlines the patient-centered approach and points towards the need for individual longer-term career curricula for clinicians and researchers in the field. Conclusion: The vision 2030 balances the ambition of physical, technological, and biological innovation as well as a comprehensive, patient-centered, and collaborative approach towards radiotherapy & radiation oncology in Germany.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p–Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.