Three Dimensional Conformal Radiation Therapy involves the following key steps:
1. Image acquisition using CT or MRI to obtain 3D anatomical information of the patient.
2. Target and critical structure delineation through image segmentation to define volumes of interest.
3. Treatment planning using a 3D planning system to design optimized beam arrangements and apertures that conform the dose distribution to the target volume while minimizing dose to surrounding tissues.
4. Plan evaluation using tools like isodose distributions, dose-volume histograms and color washes to evaluate the dose coverage of targets and sparing of critical structures before finalizing the plan.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
The vmat vs other recent radiotherapy techniquesM'dee Phechudi
VMAT is a new type of intensity-modulated radiation therapy (IMRT) treatment technique that uses the same hardware (i.e. a digital linear accelerator) as used for IMRT or conformal treatment, but delivers the radiotherapy treatment using a rotational or arc geometry rather than several static beams.
This technique uses continuous modulation (i.e. moving the collimator leaves) of the multileaf collimator (MLC) fields, continuous change of the fluence rate (the intensity of the X rays) and gantry rotation speed across a single or multiple 360 degree rotations
The vmat vs other recent radiotherapy techniquesM'dee Phechudi
VMAT is a new type of intensity-modulated radiation therapy (IMRT) treatment technique that uses the same hardware (i.e. a digital linear accelerator) as used for IMRT or conformal treatment, but delivers the radiotherapy treatment using a rotational or arc geometry rather than several static beams.
This technique uses continuous modulation (i.e. moving the collimator leaves) of the multileaf collimator (MLC) fields, continuous change of the fluence rate (the intensity of the X rays) and gantry rotation speed across a single or multiple 360 degree rotations
Conventional radiotherapy treatments are delivered with radiation beams that are of uniform intensity across the field (within the flatness specification limits). Wedges or compensators are used to modify the intensity profile to offset contour in irregularities and produce more uniform composite dose distributions such as in techniques using wedges. This process of changing beam intensity profile to meet the goals of a composite plan is called intensity modulation
IMRT refers to a radiation therapy technique in which nonuniform fluence is delivered to the patient from any given position of the treatment beam to optimize the composite dose distribution. The optimal fluence profiles for a given set of beam directions are determined through inverse planning. The fluence files thus generated are electronically transmitted to the linear accelerator, which is computer controlled, to deliver intensity modulated beams (IMBs) as calculated.
Intensity Modulated Radiation Therapy (IMRT) is an advanced mode of high-precision radiotherapy that uses computer-controlled linear accelerators to deliver precise radiation doses to a malignant tumor or specific areas within the tumor by reducing radiation dose to the nearby normal tissues.
This seminar is presented as a part of weekly journal club and seminar presented in Apollo Hospital,Kolkata Department of Radiation Oncology.This seminar is moderated by Dr Tanweer Shahid.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
2. • 3 D CRT is based on 3-D
anatomic information and use
dose distributions that conform
as closely as possible to the
target volume in terms of
adequate dose to the tumor and
minimum possible dose to
adjacent normal tissue
3.
4. Overwiew
• Main distinction between treatment planning
of 3-D CRT and conventional radiation therapy
– it requires
: 3-D anatomic information
: treatment-planning system that
allows optimization of dose distribution which
meets the clinical objectives.
8. • Preplanning process
• proposed treatment position of the patient is
determined.
• immobilization device is fabricated.
• It is an Important step--Errors may occur if
patients are inadequately immobilized, with
resultant treatment fields inaccurately aligned
from treatment to treatment .
11. • Image acquisition is the foundation of 3 D
panning.
• The anatomic information is usually obtained
in the form of closely spaced transverse
images, which can be processed to reconstruct
anatomy in any plane, or in three dimensions
13. CT Image- most commonly used
• CT image -reconstructed from a
matrix of relative linear
attenuation coefficients
measured by the CT scanner.
14. CT SIMULATOR
• Images are acquired on a
dedicated CT machine called
CT simulator with following
features
– A large bore (75-85cm) to
accommodate various treatment
positions along with treatment
accessories.
– A flat couch insert to simulate
treatment machine couch.
– A laser system consisting of
• Inner laser
• External moving laser to
position patients for
imaging & for marking
- A graphic work station
15. • CT is done with patient in the treatment position with
immobilization device
• Radio opaque fiducial are placed . Intention is to place these
initial marks as close to final isocentre as possible
• These fiducial assist in any coordinate transformation needed as
a result of 3D planning and eventual plan implementation.
• The planning CT data set is transferred to a 3D-TPS or
workstation via a computer network.
19. • The term image registration -- a process of
correlating different image data sets to identify
corresponding structures or regions.
• Allows full voxel to voxel intensity match
• Image Fusion automatically correlates thousands
of points from two image sets, providing true
volumetric fusion of anatomical data sets.
20. • For example--, mapping of structures seen in MRI onto
the CT images.
• Various registration techniques include
– Point-to-point fitting,
– Line or curve matching
– Surface or topography matching
– Volume matching
23. Segmentation
• slice-by-slice delineation of anatomic regions of
interest-- external contours, targets, critical
normal structures, anatomic landmarks, etc.
• The radiation oncologist draws the target
volumes in each slice with appropriate margins to
include visible tumor, the suspected tumor
spread, and patient motion uncertainties.
• The segmented regions can be rendered in
different colors
24. • one of the most laborious but important
processes in treatment planning.
• It requires clinical judgment, which cannot be
automated or completely image based.
• It should not be delegated to personnel other
than the physician in charge of the case, the
radiation oncologist.
27. • volumes defined prior to treatment planning
– Gross tumor volume (GTV).
– Clinical target volume (CTV).
• Defined during the treatment planning process
– Planning target volume (PTV).
– Organs at risk.
• As a result of treatment planning, volumes described.
– Treated volume (TV).
– Irradiated volume (IRV).
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43. Beam aperture design
aided by
• the BEV ( Beam’s eye view)capability of the 3-
D treatment-planning system
• DRR
44. Digitally Reconstructed Radiograph-DRR
• A synthetic radiograph produced by tracing ray-lines from a
virtual source position through the CT data to a virtual film
plane .
• It is analogous to conventional simulation radiographs.
45. • DRR is used
– for treatment portal design
– for verification of treatment
portal by comparison with port
films or electronic portal
images
46. Beam Eye View-BEV
• In BEV observer’s viewing point is
at the source of radiation looking
out along axis of radiation beam.
• Targets and critical normal
structures visible in different
colors through segmentation can
be viewed from different
directions in planes perpendicular
to the beam's central axis.
– Demonstrates geometric coverage of
target volume by the beam
– Shielding & MLCs are designed on
BEV
– Useful in identifying best gantry,
collimator, and couch angles to
irradiate target & avoid adjacent
normal structures
47.
48. Beam apertures can be designed
• Automatically - the user sets a uniform
margin around the PTV.
• Manual- when its needed to draw a
nonuniform margin
• Generally, a 2-cm margin between the PTV
and the field edge ensures better than 95%
isodose coverage of the PTV
50. • For planning, the 3D TPS must have the capability to simulate
each of the treatment machine motion functions, including
– Gantry angle,
– Collimator length, width & angle,
– MLC leaf settings,
– Couch latitude, longitude, height & angle
51. FORWARD PLANNING
• For 3D CRT forward planning is used.
• Beam arrangement is selected.
• Using BEV, beam aperture is designed
• Dose is prescribed.
• 3D dose distribution is calculated.
• Then plan is evaluated.
• Plan is modified based on dose distribution evaluation, using
various combinations of
– Beam , collimator & couch angle,
– Beam weights &
– Beam modifying devices (wedges, compensators) to get desired dose
distribution.
52. • Three-dimensional treatment planning
encourages the use of multiple fields because
targets and critical structures can be viewed in
the BEV configuration individually for each field.
• Multiple fields removes the need for using ultra-
high-energy beams (>10 MV), which are required
when treating thoracic or pelvic tumors with only
two parallel opposed fields
53. • Using a large number of fields (greater than
four) creates the problem of
- designing an excessive number of beam-
shaping blocks
- requiring longer setup times
-Carrying so many heavy blocks creates a
nuisance for therapists who have to guard
against dropping a block accidentally or using
a wrong block.
54. • A good alternative to multiple
field blocking is the use of a
multileaf collimator (MLC)
• A field drawn on a BEV printout
can be digitized to set the MLC
setting.
• BEV field outlines can also be
transmitted electronically to the
accelerator to program the MLC.
56. Plan Optimisation
• Optimisation refers to the technique of finding
the best physical and technically possible
treatment plan to fulfill the specified physical
and clinical criteria
• An optimal plan should deliver tumoricidal
dose to the entire tumor and spare all the
normal tissues.
57. PLAN EVALUATION
• Tools used in the evaluation of the planned
dose distribution:
• Isodose lines
• Color wash
• DVHs (Dose volume histograms )
– Dose distribution statistics
58. Isodose curves
• Dose distributions of
competing plans are
evaluated by viewing
isodose curves in individual
slices, orthogonal planes
(e.g., transverse, sagittal,
and coronal), or 3-D isodose
surfaces.
59. Colour wash
• Spectrum of colors superimposed on
the anatomic information represented
by modulation of intensity
– Gives quick over view of dose
distribution
– Easy to assess overdosage in
normal tissue that are not
contoured.
– To assess dose heterogeneity
inside PTV
• Slice by slice evaluation of dose
distribution can be done
60. Dose volume histograms
• DVHs summarize the information contained in
the 3-D dose distribution & quantitatively
evaluates treatment plans.
• DVHs are usually displayed in the form of ‘per
cent volume of total volume’ against dose.
• The DVH may be represented in two forms:
– Cumulative integral DVH
– Differential DVH.
61. CUMULATIVE DVH-more useful
• It is plot of volume of a given
structure receiving a certain
dose.
• Any point on the cumulative
DVH curve shows the volume
of a given structure that receives
the indicated dose or higher.
• It start at 100% of the volume
for zero dose, since all of the
volume receives at least more
than zero Gy.
62. DIFFERENTIAL DVH
• The direct or differential DVH is
a plot of volume receiving a dose
within a specified dose interval
(or dose bin) as a function of
dose.
• It shows extent of dose variation
within a given structure.
• The ideal DVH for a target
volume would be a single column
indicating that 100% of volume
receives prescribed dose.
• For a critical structure, the DVH
may contain several peaks
indicating that different parts of
the organ receive different doses.
DVH - target vol.
DVH - OAR
63. 3-D DOSE CLOUD
• Map isodoses in three
dimensions and
overlay the resulting
isosurface on a 3-D
display with surface
renderings of target
& other contoured
organs.
64. Dose statistics
• It provide quantitative information on the volume of the target or critical
structure and on the dose received by that volume.
• These include:
– The minimum dose to the volume
– The maximum dose to the volume
– The mean dose to the volume
• Useful in dose reporting.
65.
66.
67.
68. PLAN IMPLEMENTATION
• Once the treatment plan has been evaluated &
approved, documentation for plan implementation
must be generated.
• It includes
– beam parameter settings transferred to the treatment
machine’s record and verify system,
– MLC parameters communicated to computer system
that controls MLC system of the treatment machine,
– DRR generation & printing or transfer to an image
database.
69. Plan implementation
• After Physician contuors target volumes and determination
of treatment isocentre is done
• Couch shifts ( distances in three directions ) between
reference marks drawn on CT Scanner and treatment
centre are then calculated
• On first day of treatment , patient is first positioned to
initial refernce marks and then shifted to treatment
isocentre using the calculated shifts .
• The treatment isocentre is then marked on the patient
70. Position verification
• Patient position is verified and thus corrected using
EPID ( electronic portal imaging device)
• Both the field and the bony anatomy are matched
sequentially to give an estimate of error.
71. Online and offline corrections
• refer to whether the patient is on the
treatment couch while the verification is being
done and whether the correction would be
applied to the same or subsequent sessions
72. Offline corrections
• images acquired before treatment and matched
to the reference image at a later time point.
• Aims to determine the individual systematic
setup error and thus reduce it.
• When combined with setup data of other
patients treated under the same protocol, it helps
define the population standard error for that
treatment in that institution.
• PTV margins in an institution depend on these
determinations of individual and population
systematic errors
73. Online corrections
• Acquisition of images and their verification and
correction prior to the day’s treatment.
• Aims to reduce both random and systematic errors.
• The treatment site and the expected magnitude of
error may determine the frequency of online imaging.
• Sites where large daily shifts are anticipated (abdomen,
pelvis, and thorax) or where even slight shifts will alter
the dose distribution within adjacent critical structures
(paraspinal tumors, intracranial tumors in close
proximity to optic structures) are best managed with
daily imaging
74.
75. Dose calculation
• Dose calculation algorithms ….three broad
categories:
(a) correction based,
(b) model based, and
(c) direct Monte Carlo.
• Direct Monte Carlo - most accurate method
for treatment planning.