Evolving Clinical Cancer Radiotherapy: Concerns Regarding Normal Tissue Protection and Quality Assurance

Radiotherapy, which is one of three major cancer treatment methods in modern medicine, has continued to develop for a long period, more than a century. The development of radiotherapy means allowing the administration of higher doses to tumors to improve tumor control rates while minimizing the radiation doses absorbed by surrounding normal tissues through which radiation passes for administration to tumors, thereby reducing or removing the incidence of side effects. Such development of radiotherapy was accomplished by the development of clinical radiation oncology, the development of computers and machine engineering, the introduction of cutting-edge imaging technology, a deepened understanding of biological studies on the effects of radiation on human bodies, and the development of quality assurance (QA) programs in medical physics. The development of radiotherapy over the last two decades has been quite dazzling. Due to continuous improvements in cancer treatment, the average five-year survival rate of cancer patients has been close to 70%. The increases in cancer patients' complete cure rates and survival periods are making patients' quality of life during or after treatment a vitally important issue. Radiotherapy is implemented in approximately 1/3 to 2/3s of all cancer patients; and has improved the quality of life of cancer patients in the present age. Over the last century, as a noninvasive treatment, radiotherapy has unceasingly enhanced complete tumor cure rates and the side effects of radiotherapy have been gradually decreasing, resulting in a tremendous improvement in the quality of life of cancer patients.

Multi-institutional analysis of T3 subtypes and adjuvant radiotherapy effects in resected T3N0 non-small cell lung cancer patients

PURPOSE: We evaluated the prognostic significance of T3 subtypes and the role of adjuvant radiotherapy in patients with resected the American Joint Committee on Cancer stage IIB T3N0M0 non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: T3N0 NSCLC patients who underwent resection from January 1990 to October 2009 (n = 102) were enrolled and categorized into 6 subgroups according to the extent of invasion: parietal pleura chest wall invasion, mediastinal pleural invasion, diaphragm invasion, separated tumor nodules in the same lobe, endobronchial tumor <2 cm distal to the carina, and tumor-associated collapse. RESULTS: The median overall survival (OS) and disease-free survival (DFS) were 55.3 months and 51.2 months, respectively. In postoperative T3N0M0 patients, the tumor size was a significant prognostic factor for survival (OS, p = 0.035 and DFS, p = 0.035, respectively). Patients with endobronchial tumors within 2 cm of the carina also showed better OS and DFS than those in the other T3 subtypes (p = 0.018 and p = 0.016, respectively). However, adjuvant radiotherapy did not cause any improvement in survival (OS, p = 0.518 and DFS, p = 0.463, respectively). Only patients with mediastinal pleural invasion (n = 25) demonstrated improved OS and DFS after adjuvant radiotherapy (n = 18) (p = 0.012 and p = 0.040, respectively). CONCLUSION: The T3N0 NSCLC subtype that showed the most favorable prognosis is the one with endobronchial tumors within 2 cm of the carina. Adjuvant radiotherapy is not effective in improving survival outcome in resected T3N0 NSCLC.

Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy / 의학물리

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy / 의학물리

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.