Knowledge-based treatment planning: fundamentally different, or more of the same?

Transcription

1 Knowledge-based treatment planning: fundamentally different, or more of the same? Kevin L. Moore, Ph.D., DABR Associate Director of Physics Manager of Dosimetry Group

2 Disclosure Statement Patent for dosimetric predictions of dose-volume relationships in radiotherapy planning Varian Medical Systems - Licensing Agreement for KBP - Master Research Agreement - Consulting - Honoraria 2

3 Objectives 1. Discuss current understanding of plan quality variability in clinical practice 1. Provide overview of the core concepts of treatment plan quality control and Knowledge-Based Planning (KBP) 1. Demonstrate effect of KBP in various treatment sites and discuss its future in the treatment planning ecosystem 3

4 Outline What is the KB in KBP? The need for treatment plan quality control How to make quantitative predictions of plan quality Quality control in practice Quantifying the clinical costs of sub-optimal planning Knowledge-based planning 4

5 Must-have features of a knowledge base Must be quantitative o numerical data, preferable digitized! Must have discernable correlations o e.g. larger bladder = lower bladder DVH Must provide a sufficient range of previous experience With these ingredients, one has everything needed to make quantitative predictions 5

6 Does this count as a knowledge base for tx planning? 6

7 An experiment: can you pick out the bad plans? Rectum DVHs from 100 UCSD prostate patients Rx dose = 81 Gy Definition of bad : rectum could have been better spared without sacrificing target metrics (or any others) 7

8 A distinction QUANTEC and other reports regarding the radiation response of particular organs can tell us about what is bad for OARs Tracking across multiple patients can give a sense of normal behavior, for example: a) mean and standard deviation of a particular metric b) how often a particular objective is violated However, what constitutes bad in regards to plan quality is a different story. As we all know, some patients have particular anatomy that makes meeting objectives difficult, or even impossible (without sacrificing another metric). 8

9 Outline What is the KB in KBP? The need for treatment plan quality control and KBP How to make quantitative predictions of plan quality Quality control in practice Quantifying the clinical costs of sub-optimal planning Knowledge-based planning 9

10 What problem does KBP purport to solve? H&N Bilateral Neck Treatment Ipsilateral Neck Treatment PTV 95% of PTV > 95% of Rx; Max dose < 110% of Rx 95% of PTV > 95% of Rx; Max dose < 110% of Rx Spinal Cord Max dose 40 Gy Max dose 40 Gy Spinal Cord + Margin Max dose 52 Gy; < 1% (or 1 cc) exceeds 50 Gy Max dose 52 Gy; < 1% (or 1 cc) exceeds 50 Gy Optic Nerves, Optic Chiasm Max dose 54 Gy Max dose 54 Gy Brainstem Max dose 54 Gy; < 1% exceeds 60 Gy Max dose 54 Gy; < 1% exceeds 60 Gy Brain Max dose 60 Gy; < 1% exceeds 65 Gy Max dose 60 Gy; < 1% exceeds 65 Gy Retina Max dose 50 Gy; < 5% exceeds 45 Gy Max dose 50 Gy; < 5% exceeds 45 Gy Larynx As low as possible; mean dose < 45 Gy As low as possible; mean Dose <25 Gy Upper Esophagus As low as possible; mean dose < 45 Gy As low as possible; mean dose < 25 Gy Parotid As low as possible; mean dose < 26 Gy As low as possible; mean dose < 10 Gy (contralateral) Pharyngeal Constrictors As low as possible; V60 < 60 Gy As low as possible; V60 < 45 Gy Submandibular As low as possible; mean dose < 39 Gy As low as possible; mean dose < 24 Gy (contralateral) Oral Cavity As low as possible; mean dose < 35 Gy As low as possible; mean dose < 20 Gy Mandible Max 70 Gy; < 5% exceeds PTV Rx Max 70 Gy; < 5% exceeds PTV Rx Unspecified Tissue Less than PTV Rx; < 5% exceeds PTV Rx Less than PTV Rx; < 5% exceeds PTV Rx Transparent planning goals are necessary, but are they sufficient? 10

11 What problem does KBP purport to solve? H&N PTV Spinal Cord Spinal Cord + Margin Optic Nerves, Optic Chiasm Brainstem Brain Retina Larynx Upper Esophagus Parotid Pharyngeal Constrictors Submandibular Oral Cavity Mandible Unspecified Tissue Bilateral Neck Treatment 95% of PTV > 95% of Rx; Max dose < 110% of Rx Max dose 40 Gy Max dose 52 Gy; < 1% (or 1 cc) exceeds 50 Gy Max dose 54 Gy Max dose 54 Gy; < 1% exceeds 60 Gy Max dose 60 Gy; < 1% exceeds 65 Gy Max dose 50 Gy; < 5% exceeds 45 Gy As low as possible; mean dose < 45 Gy As low as possible; mean dose < 45 Gy As low as possible; mean dose < 26 Gy As low as possible; V60 < 60 Gy As low as possible; mean dose < 39 Gy As low as possible; mean dose < 35 Gy Max 70 Gy; < 5% exceeds PTV Rx Less than PTV Rx; < 5% exceeds PTV Rx upper esoph larynx PTV 56 PTV 70 Transparent planning goals are necessary, but are they sufficient? 11

12 What problem does KBP purport to solve? H&N PTV Spinal Cord Spinal Cord + Margin Optic Nerves, Optic Chiasm Brainstem Brain Retina Larynx Upper Esophagus Parotid Pharyngeal Constrictors Submandibular Oral Cavity Mandible Unspecified Tissue Bilateral Neck Treatment 95% of PTV > 95% of Rx; Max dose < 110% of Rx Max dose 40 Gy Max dose 52 Gy; < 1% (or 1 cc) exceeds 50 Gy Max dose 54 Gy Max dose 54 Gy; < 1% exceeds 60 Gy Max dose 60 Gy; < 1% exceeds 65 Gy Max dose 50 Gy; < 5% exceeds 45 Gy As low as possible; mean dose < 45 Gy As low as possible; mean dose < 45 Gy As low as possible; mean dose < 26 Gy As low as possible; V60 < 60 Gy As low as possible; mean dose < 39 Gy As low as possible; mean dose < 35 Gy Max 70 Gy; < 5% exceeds PTV Rx Less than PTV Rx; < 5% exceeds PTV Rx upper esoph larynx PTV 56 PTV 70 Transparent planning goals are necessary, but are they sufficient? (Dotted line plan was approved but not treated) 12

13 Claim: planning goals that don t take into account patient-specific anatomy cannot guarantee optimality upper esoph larynx PTV 56 PTV 70 IMRT QC Unless planning systems make trade-offs explicit, ignorance of what s possible can prevent consistently optimal planning KBP can address this problem at both input and output 13

14 Claim: planning goals that don t take into account patient-specific anatomy cannot guarantee optimality 14

15 Outline What is the KB in KBP? The need for treatment plan quality control and KBP How to make quantitative predictions of plan quality Quality control in practice Quantifying the clinical costs of sub-optimal planning Knowledge-based planning 15

16 Geometric quantification = dosimetric quantification Vineberg, K. A. et al. Is uniform target dose possible in IMRT plans in the head and neck? Int J Radiat Oncol Biol Phys 52 (5): (2002) Hunt, M.A. et al. Geometric factors influencing dosimetric sparing of the parotid glands using IMRT, Int J Radiat Oncol Biol Phys 66 (1): (2006) 16

17 Geometric quantification = dosimetric quantification Moore, K.L. et al. Experience based quality control of clinical intensity modulated radiotherapy planning, Int J Radiat Oncol Biol Phys. 81(2): (2011) 17

18 The need for IMRT quality control Need system that can identify sub-optimal plans (most typically manifested as insufficient OAR sparing) With the model rediction, we can catch suspected outliers, take corrective action (i.e. more appropriate IMRT planning objectives), and bring the OAR doses back toward expected values δ (prior) = 0.28 ± 0.24 δ (after) = 0.12 ± overlap script clinically deployed KL Moore et al, IJROBP 81, (2010) 18

19 Salvageable parotids: 3 mos. before QC vs 3 mos. after KL Moore et al, IJROBP 81, (2011) 19

20 Going further: DVH prediction in three easy steps Step 1 Identify a set of site similar training patients Step 2 Generate pdvh model from training cohort Step 3 Utilize pdvh model to obtain DVH prediction for new patient Patient 1 SS 13 SS 11 D 1 (x) SS 12 Patient N Appenzoller et al, Med Phys 39, 7446 (2012) 20

21 DVH prediction for parotids in head-and-neck Blue is clinically-approved plan Red is average model Black is gold-standard model 21

22 Re-planning Results: demonstrates Parotid correct outlier identification IMRT QC 22

23 Comparing clinical DVHs to predicted DVHs Judging one DVH against another involves knowledge of specific organ radiobiology and DVH cutpoints Examining DVH difference is useful in that it allows regions of improvement (shaded) to be easily identified and quantified 23

24 An experiment: can you pick out the bad plans? Rectum DVHs from 100 UCSD prostate patients Rx dose = 81 Gy Definition of bad : rectum could have been better spared without sacrificing target metrics (or any others) 24

25 Each of these now has an associated predicted DVH Rectum DVHs from 100 UCSD prostate patients Rx dose = 81 Gy Definition of bad : rectum could have been better spared without sacrificing target metrics (or any others) DVH that is clinically worse than its predicted DVH 25

26 How do we specify clinically worse? 26

27 Comparing clinical DVHs to predicted DVHs Judging one DVH against another involves knowledge of specific organ radiobiology and DVH cutpoints Examining DVH difference is useful in that it allows regions of improvement (shaded) to be easily identified and quantified 27

28 An experiment: can you pick out the bad plans? Definition of bad : rectum could have been better spared without sacrificing target metrics (or any others) DVH that is clinically worse than its predicted DVH red = clinical V75 > predicted V75 blue = clinical V75 < predicted V75 28

29 An experiment: can you pick out the bad plans? Rectum DVHs from 100 UCSD prostate patients Rx dose = 81 Gy Definition of bad : rectum could have been better spared without sacrificing target metrics (or any others) DVH that is clinically worse than its predicted DVH 29

30 Outline What is the KB in KBP? The need for treatment plan quality control and KBP How to make quantitative predictions of plan quality Quality control in practice Quantifying the clinical costs of sub-optimal planning Knowledge-based planning 30

31 Re-planning demonstrates correct outlier identification IMRT QC 31

32 Inter-institutional Results: Parotid QC at a small radiotherapy clinic All five patients replanned showed similar results Table 3. Average Reduction in V65 and V40 for Rectum and Bladder Organ V65(orig)-V65(replan) dv65 V40(orig)-V40(replan) dv40 Rectum 4.8%±2.3% 0.9%±1.1% 17.9%±10.3% 0.7%±1.4% Bladder 3.4%±2.1% 0.4%±0.5% 6.0%±2.8% 0.6%±0.9% Appenzoller L.M., et. al. Predictive DVH models developed at a large institution impact clinically relevant DVH parameters in IMRT plans at an unrelated radiotherapy facility, Oral presentation AAPM 2013 (BEST IN PHYSICS). 32

33 Outline What is the KB in KBP? The need for treatment plan quality control and KBP How to make quantitative predictions of plan quality Quality control in practice Quantifying the clinical costs of sub-optimal planning Knowledge-based planning 33

34 How to study effects on a large scale? 34

35 Schema for post hoc quality study on RTOG 0126 Moore KL et al, Suboptimal planning adds substantial risk of normal tissue complications: a secondary study on RTOG 0126, (will be presented at ASTRO 2014) 35

36 Bladder and rectum DVH comparisons Moore KL et al, Suboptimal planning adds substantial risk of normal tissue complications: a secondary study on RTOG 0126, (will be presented at ASTRO 2014)

37 Validation of NTCP predictions

38 How did the re-plans differ from the original plans? Moore KL et al, Suboptimal planning adds substantial risk of normal tissue complications: a secondary study on RTOG 0126, (will be presented at ASTRO 2014)

39 Outline What is the KB in KBP? The need for treatment plan quality control and KBP How to make quantitative predictions of plan quality Quality control in practice Quantifying the clinical costs of sub-optimal planning Knowledge-based planning 39

40 Knowledge-based planning (KBP) TIME SS 1? DOSE 1 SS 3? DOSE 3... SS N? DOSE N SS 2? DOSE 2 SS 4? DOSE 4 SS N+1??? KBP engine o o Minimally, DVH predictions can guide the planner in selection of optimization objectives In its ideal form, KBP takes the best practices of prior experience and applies this quantitatively to the optimization of new patients 40

41 KBP vs treatment plan QC Knowledge-based planning is an extension of treatment plan quality control, incorporating the dose-volume predictions into the optimization loop PTV Knowledge-based DVH predictions bladder rectum Knowledge-based optimization parameters 41

42 More examples of KBP: GYN IMRT small bowel pelvic bone marrow 42

43 More examples of KBP: stereotactic radiosurgery 43

44 KBP: fundamentally different or more of the same? KBP is fundamentally different in its use of prior information. The quantitative predictions of expected plan quality parameters (beyond population-based objectives) is new and will be a change. In regards to the mechanics of plan optimization, much if not all of KBP can be deployed in existing optimization platforms. The largest improvements for the field will likely be seen in reducing the chances of fundamentally flawed treatment plans from treating patients As an engine for automation, KBP will bring changes to the role of treatment planners in the clinic 44

45 Conclusions Poor quality plans can have quantifiably bad and wholly unnecessary effects on our patients. Knowledge-based dose predictions have the ability to solve this problem before it even becomes a problem. Irrespective of manual or automated treatment plan generation, there is a role for treatment plan quality assurance to ensure consistent tradeoffs are made and delivery technology is optimally used. Acknowledgements: Lindsey (Appenzoller) Olsen, M.S. Sasa Mutic, Ph.D. Jun Tan, Ph.D. Jeff Michalski, M.D. Satomi Shiraishi, Ph.D. Dosimetrists at UCSD and WashU 45

46 Schema: use predictive DVHs to find best plans 1. Train average model on all 100 patients 2. Find the top 20% of patients that spared the rectum compared to the average model 3. Train new refined model with these patients dotted = predicted solid = clinical VERDICT: INCLUDE 46

47 Schema: use predictive DVHs to find best plans 1. Train average model on all 100 patients 2. Find the top 20% of patients that spared the rectum compared to the average model 3. Train new refined model with these patients dotted = predicted solid = clinical VERDICT: EXCLUDE 47

48 Schema: use predictive DVHs to find best plans 1. Train average model on all 100 patients 2. Find the top 20% of patients that spared the rectum compared to the average model 3. Train new refined model with these patients dotted = refined prediction solid = clinical 48