Homogeneity Index (HI) is an objective tool to analyz the uniformity of dose distribution in the target volume. Various formulae have been described in literature for its calculation but there is paucity of data regarding the ideal formula and the factors affecting this index. This study was undertaken to analyze HI in our patients using various formulae and to find out the co-relation between HI and prescribed dose, target volume and target location. A retrospective review of 99 patients was performed. HI was calculated using five different formulae (A-E). The patients were divided in five groups each, based on prescribed dose, target volume and target location and mean HI of each group was analysed to find the co-relation between these factors and HI. When there were multiple target volumes the primary target volume was studied. The statistical calculation was done using SPSS version 16.0. Ninety nine patients were found evaluable with 75 males and 24 females. Ninety five patients were treated with radical intent and four with palliative intent. The sites treated were head and neck (46.4%), Pelvis (17.1%), brain (15.1%), abdomen (12.1%), and thorax (6.1%). The mean prescribed dose was 4304 cGy (centiGray) and the mean target volume was 476.2 cc. The mean value of HI was 1.21, 2.08, 30.13, 21.51 and 1.27 with different formulae. There was considerable agreement between HI calculated using various formulae specially the formulae considering prescribed dose (C, D). On statistical analysis, there was no significant co-relation between the location and volume of target but there was a trend toward better HI with increasing prescribed dose. Future studies with more number of patients can confirm our results.

Objective: The present study evaluated the efficacy and toxicity of adaptive radiotherapy (RT) among patients with head and neck cancer. Methods: 36 patients eligible for radical RT underwent RT planning scans and were planned for 54-Gy dose to both high-risk and low-risk target volumes in Phase I. All patients underwent a second (adaptive) scan during the fifth week of RT. Phase II plans for 16 Gy to high-risk planning target volume were developed on these mid-treatment scans. The primary end point was local response. Disease-free survival (DFS), overall survival (OS) and treatment-related morbidity were secondary end points.

No abstract

BACKGROUND: Mucositis, a radiotherapy‐associated toxicity, is an important factor determining morbidity and treatment compliance. Gastrointestinal mucositis in patients undergoing radiotherapy may also depend on time of administration of radiation in addition to several other factors. The presence of any correlation between the severity of acute gastrointestinal mucositis in cervical carcinoma patients and the time of irradiation was prospectively evaluated. METHODS: A total of 229 patients with cervical carcinoma were randomized to morning (8:00‐10:00 AM) and evening (6:00‐8:00 PM) arms. The incidence of mucositis in the 2 arms was assessed and reported in terms of various grades of diarrhea. RESULTS: Overall (grade I‐IV) as well as higher grade (III and IV) diarrhea was found to be significantly increased in the morning arm as compared with the evening arm (overall: 87.39 % vs 68.18 %, P < .01; higher grade: 14.29% vs 5.45%, P < .05). Other radiation‐induced toxicity was also higher in the morning arm, but its occurrence in the 2 arms did not differ significantly (13.45% vs 12.73%, P > .05). After completion of treatment, patients' response to radiation in the 2 arms was similar (P > .05). CONCLUSIONS: The significant difference in the incidence of higher grade diarrhea between the morning and evening arms is indirect evidence of the influence of circadian rhythm on the intestinal mucosa of the human intestine. This knowledge may facilitate treating patients with decreased toxicity to the intestinal mucosa. Cancer 2010. © 2010 American Cancer Society.

The observed incidence of grade III/IV mucositis in morning vs. evening irradiated patients may be because of the existence of circadian rhythm in the cell cycle of normal mucosa. This knowledge may provide a possibility of treating the patients with decreased toxicity to oral mucosa.

Axillary levels I and II (lower axilla) receive substantial amount of incidental radiation doses with all the three techniques; however, conformal techniques (IMRT, 3DCRT) deliver significantly lesser incidental radiation to lower axilla than ST technique.

Stereotactic body radiotherapy (SBRT) is being increasingly utilized in the treatment of prostate cancer. With the advent of high-precision radiosurgery systems, it is possible to obtain dose distributions akin to high-dose rate brachytherapy with SBRT. However, urethral toxicity has a significant impact on the quality of life in patients with prostate cancer. Contouring the male urethra on a CT scan is difficult in the absence of an indwelling catheter. In this pictorial essay, we have used the MRI obtained for radiotherapy planning to aid in the delineation of the male urethra and have attempted to define guidelines for the same.

Objective: Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it. Methods and materials: Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made. Results: The article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it’s the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future. Conclusion: AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians.