Intensity modulated radiotherapy for high risk prostate cancer based on sentinel node SPECT imaging for target volume definition

BACKGROUND: The RTOG 94-13 trial has provided evidence that patients with high risk prostate cancer benefit from an additional radiotherapy to the pelvic nodes combined with concomitant hormonal ablation. Since lymphatic drainage of the prostate is highly variable, the optimal target volume definition for the pelvic lymph nodes is problematic. To overcome this limitation, we tested the feasibility of an intensity modulated radiation therapy (IMRT) protocol, taking under consideration the individual pelvic sentinel node drainage pattern by SPECT functional imaging. METHODS: Patients with high risk prostate cancer were included. Sentinel nodes (SN) were localised 1.5–3 hours after injection of 250 MBq (99m)Tc-Nanocoll using a double-headed gamma camera with an integrated X-Ray device. All sentinel node localisations were included into the pelvic clinical target volume (CTV). Dose prescriptions were 50.4 Gy (5 × 1.8 Gy / week) to the pelvis and 70.0 Gy (5 × 2.0 Gy / week) to the prostate including the base of seminal vesicles or whole seminal vesicles. Patients were treated with IMRT. Furthermore a theoretical comparison between IMRT and a three-dimensional conformal technique was performed. RESULTS: Since 08/2003 6 patients were treated with this protocol. All patients had detectable sentinel lymph nodes (total 29). 4 of 6 patients showed sentinel node localisations (total 10), that would not have been treated adequately with CT-based planning ('geographical miss') only. The most common localisation for a probable geographical miss was the perirectal area. The comparison between dose-volume-histograms of IMRT- and conventional CT-planning demonstrated clear superiority of IMRT when all sentinel lymph nodes were included. IMRT allowed a significantly better sparing of normal tissue and reduced volumes of small bowel, large bowel and rectum irradiated with critical doses. No gastrointestinal or genitourinary acute toxicity Grade 3 or 4 (RTOG) occurred. CONCLUSION: IMRT based on sentinel lymph node identification is feasible and reduces the probability of a geographical miss. Furthermore, IMRT allows a pronounced sparing of normal tissue irradiation. Thus, the chosen approach will help to increase the curative potential of radiotherapy in high risk prostate cancer patients

Image-guided adaptive treatment planning for intensity modulated radiotherapy

Es wird ein Optimierungskonzept vorgestellt, das - durch die Einbeziehung der zeitlichen geometrischen Variationen der Patientenanatomie in die Bestrahlungsplanung - eine weit grössere Anzahl an Freiheitsgraden ausnutzt, um bei der Bestrahlung mit intensitätsmodulierten Strahlenfeldern (IMRT) die gleichzeitige Schonung von Normalgewebe und die Maximierung der Tumordosis zu erreichen. Die probabilistische Beschreibung der Patientengeometrien durch lokale Deformationswahrscheinlichkeiten ermöglicht die Optimierung des zeitlichen Erwartungswertes der Dosisverteilung bezüglich der biologischen und medizinisch evidenten Wirkung im Patienten. Durch die multiple Anwendung heute gängiger Bildgebungsverfahren (Feldkontrollaufnahmen, CT, MRI) wird die individuelle Beschreibung von Lagerungsfehlern und innerer Organbewegung möglich. Mit Hilfe biomechanischer Modelle können sogar die individuellen Verlagerungen einzelner Volumenelemente der Gewebematrix näherungsweise bestimmt, und damit Aussagen über die Statistik der lokalen geometrischen Variationen gewonnen werden. Die so erhaltene individuelle, geometrische Information kann auf einfache Weise für die Abschätzung des wahrscheinlichen Behandlungsergebnisses und damit für die probabilistische Optimierung intensitätsmodulierter Bestrahlungspläne genutzt werden. Es werden verschiedene, adaptive Strategien zur off-line Rückkopplung von Bildinformation in die IMRT-Bestrahlungsplanung an mehreren Patienten mit Prostatakarzinom studiert. Wie sich zeigt, lassen sich wesentliche Charakteristika der individuellen geometrischen Variationen bereits aus wenigen Bildgebungen im frühen Behandlungsverlauf robust abschätzen und effektiv in der adaptiven Planung berücksichtigen.An optimization concept for intensity modulated radiotherapy (IMRT) is presented which makes use of a patient's temporal geometrical variations in order to achieve simultaneous normal tissue sparing and maximum tumour control. The probabilistic description of the patient anatomy by means of a local deformation probability density allows to optimize the temporal expectation value of a dose distribution with respect to the biological effect to the irradiated tissue. Multiple imaging (portal images, CT, MRI) is used for the individual quantification of setup errors and internal organ motion. By means of a biomechanical model the temporal displacements of small volume elements of the tissue matrix can be approximated and are used to quantify the local geometrical variations in a patient. Thus, the image infomation is used for a simple estimation of the most probable treatment outcome and for the probabilistic optimization of intensity modulated radiation beams. Several adaptive strategies for off-line image feedback into IMRT planning are investigated on few patients with prostate cancer. It is shown that it is possible to make a robust estimate of a patient's individual geometrical variations in the early course of a radiation treatment from only a small number of extra images

Computer-Assisted Differential Diagnosis of Pyoderma Gangrenosum and Venous Ulcers with Deep Neural Networks

(1) Background: Pyoderma gangrenosum (PG) is often situated on the lower legs, and the differentiation from conventional leg ulcers (LU) is a challenging task due to the lack of clear clinical diagnostic criteria. Because of the different therapy concepts, misdiagnosis or delayed diagnosis bears a great risk for patients. (2) Objective: to develop a deep convolutional neural network (CNN) capable of analysing wound photographs to facilitate the PG diagnosis for health professionals. (3) Methods: A CNN was trained with 422 expert-selected pictures of PG and LU. In a man vs. machine contest, 33 pictures of PG and 36 pictures of LU were presented for diagnosis to 18 dermatologists at two maximum care hospitals and to the CNN. The results were statistically evaluated in terms of sensitivity, specificity and accuracy for the CNN and for dermatologists with different experience levels. (4) Results: The CNN achieved a sensitivity of 97% (95% confidence interval (CI) 84.2–99.9%) and outperformed dermatologists, with a sensitivity of 72.7% (CI 54.4–86.7%) significantly (p < 0.03). However, dermatologists achieved a slightly higher specificity (88.9% vs. 83.3%). (5) Conclusions: For the first time, a deep neural network was demonstrated to be capable of diagnosing PG, solely on the basis of photographs, and with a greater sensitivity compared to that of dermatologists