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The disadvantage of visualizing tomography by slices is that an important attribute of the object, its volume, is not easily perceived or measured. In oncology this creates a problem, which is addressed here: if early detection and response to treatment are an important prognostic element, then volume is important. The literature has proposed surrogates to volume derived from measures on slices, but geometrically they are not well founded. Actual volume analysis is not complex, and the proposed method applies equally well to organs as to tumors. Volume based measures are more sensitive than individual SUV values, of which the commonly most used is the maximum Standardized Uptake Value (SUV
_{m}). If the tumor volume is defined, it can be replaced by the total tumor SUV (SUV
_{t}). If the metric for change is the ratio after/(before + after), in the patient population analyzed here, the SUV
_{m} metric averages 0.132 for response and 0.662 for progression, the total SUV
_{t} range is 0.069 to 0.734. In contrast to SUV
_{t}, SUV
_{m} is based on a weak sampling method since it is based on the value of a single voxel of more than 10 million.

The image of an object (in object space) is the mapping of some of the object’s attribute in image space. This mapping is perfect, only if the mapping is one to one and if the relative positioning (neighbor to neighbor relative position) or coherence is maintained. The first condition would require perfect spatial resolution. Short of that, the mapping is one to many (and by extension many to one). In projection images (a mapping from a 3D object space into a 2D image space) the mapping is not only one to many, but also structurally many to one.

Modern tomographic images computed tomography, Single Photon Computed Tomography and Positron Emission Tomography (CT, SPECT, and PET) map 3D to 3D. Image analysis is not as much affected by overlapping structures when based on image slices. However, the historic evolution of CT and SPECT, and later of PET/CT has started with a technical bias; DICOM was developed to transfer two-dimensional images (e.g. X-rays) and the data structure of CT and SPECT remained a stack of two-dimensional images. The paradox is that an image of a three-dimensional object was reduced to a stack of two-dimensional slices. This reduction, in many cases, is reinforced by the fact that the image is not isomorphic in the third dimension (the distance between slices is not the same as the distance between adjacent points within slices), and that most analytic tools (regions of interest, relative quantification) assume that the data are two-dimensional [

The effect on oncology is not good; early detection is based on the assumption that the growth of tumor enlarges the tumor mass and makes treatment less effective [

In FDG/PETCT (^{18}F-fluoroDeoxyGlucose/Positron Emission Tomography with Computed Tomography) as applied in lymphoma, historically the evaluation of the disease stage and response to treatment has been complex. At first, disease was considered present if focal or diffuse FDG uptake above background in a location was not explained by anatomy or physiology [_{m}) was generally used [

Eventually the Maximum Standard Uptake Value (SUV_{m}), and changes in SUV_{m} (DSUV_{m}) emerged to eliminate interobserver variation and standardise response to treatment [

Volume was introduced as a strange two-dimensional surrogate by the Lugano classification [

In this paper we attempt to introduce different metrics. The SUV_{m} as the maximum SUV over all tumors, the total volume of all detected tumors (expressed in liters) and the sum of all the SUV values in all tumors (SUV_{t}). A purely mathematical derivation is the average SUV_{a}, which is in general totally defined by SUV_{t} and liter

In addition the change is expressed as an index which is the ratio of the new metric divided by the sum of the old metric plus the new metric. The new metric precisely maps into the classical descriptions of response (

All the quantification is based on a volumetric search, after the elimination of normal organs.

The study includes 17 consecutive lymphoma patients undergoing 48 18F-FDG PET/CT scans in 24 pairs, composed of one before and after treatment scan, or subsequent studies during surveillance. The median time between 2 studies is 79 days, ranging from 21 to 240 days.

Targets are either organs or tumor. The targets are delineated by the operator in 3 orthogonal maximum intensity projections (_{m}, SUV_{t}, SUV_{a}) and total volume (liter), and subsequently erased.

After this only marrow activity and unspecified normal and fatty tissue remain. Marrow is handled in toto as an individual tumor. The metrics are evaluated for change by an index (

After × 100 Before + After

The results of this equation are bound between 0 and 100.

Index (%) | Significance |
---|---|

0.00 | complete response |

Between 0 and 50 | Partial response |

0.50 | Stable disease |

Between 50 and 100 | Progression |

100 | Recurrence |

The first observation is that the bone marrow response, complete (CR) or partial PR) or evolution, progression (PD) or recurrence (RC) does not follow the evolution of solid tumors [_{m}, SUV_{t} and Liter. This is illustrated in

The indexes yield not identical information, even within solid tumors, but the results generally correlate. Within marrow, the correlation is weaker for the indexes derived from SUV_{m} in relation to those derived from SUV_{t} and Liter (

ANOVA: The metrics differ not so much within lesions as between lesions | |||||
---|---|---|---|---|---|

Source of Variation | SS | df | MS | F | P-value |

Marrow vs. Non-marrow | 4964.19 | 1 | 4964.19 | 6.20 | 0.014 |

SUV_{m} vs. SUV_{t} vs. Liter | 38.82 | 2 | 19.41 | 0.02 | 0.976 |

Interaction | 131.29 | 2 | 65.65 | 0.08 | 0.921 |

SUV_{m} | SUV_{t} | Liter | |||||||
---|---|---|---|---|---|---|---|---|---|

Marrow | No Marrow | P | Marrow | No Marrow | p | Marrow | No-Marrow | P | |

CR | 41.21 | 0.00 | 0.0002 | 45.18 | 0.00 | 0.007 | 45.487 | 0.000 | 0.006 |

PR | 37.76 | 35.71 | 0.7453 | 40.54 | 19.18 | 0.081 | 41.214 | 21.200 | 0.098 |

SD | 48.14 | 50.00 | 0.2283 | 35.61 | 50.00 | 0.267 | 36.540 | 50.000 | 0.284 |

PD | 57.75 | 76.38 | 0.3433 | 69.68 | 85.18 | 0.673 | 68.813 | 85.520 | 0.680 |

Metrics | Solid Tumor | Marrow | ||||
---|---|---|---|---|---|---|

SUVmNM | SUVtNM | LiterNM | SUVmM | SUVtM | LiterM | |

SUV_{m}NM | 1 | |||||

SUV_{t}NM | 0.943 | 1 | ||||

LiterNM | 0.942 | 0.998 | 1 | |||

SUV_{m}M | 0.372 | 0.400 | 0.413 | 1 | ||

SUV_{t}M | 0.157 | 0.055 | 0.064 | 0.643 | 1 | |

LiterM | 0.174 | 0.066 | 0.076 | 0.619 | 0.993 | 1 |

Fat | body | Lean | body | |
---|---|---|---|---|

A | B | A | B | |

Mean | 0.17 | 0.63 | 0.17 | 0.59 |

Median | 0.15 | 0.15 | 0.51 | 0.53 |

SD | 0.08 | 0.11 | 0.49 | 0.29 |

Min | 0.03 | 0.08 | 0.31 | 0.38 |

Max | 0.45 | 0.56 | 2.88 | 1.88 |

Range | 0.42 | 0.48 | 2.57 | 1.50 |

T-test | P = | 0.647 | P = | 0.731 |

The thresholding limits used visually to delineate organs, tumor, fatty and lean bodies are expressed in the original metrics (SUV). _{t} but not limit or SUV_{a}.

This paper addresses three old problems. First, the use of SUV_{m} as an attempt to classification is somewhat deficient. With exploration in 2D slices, how does one know that the search for the maximum has been successful? The search is handicapped by being performed mainly on transverse slices, in lesions whose limits have not been set.

In addition, DSUV_{m} assumes that the locations match before and after. In our case, the definition is not that SUV_{m} before and after lie in the same voxel, but that they originate in the same set of tumors, which was searched over the total delineated volume.

Second, change cannot be well defined by (Before-after/before) because it does not match the response concepts of oncology. In addition, for a fixed (Before-after), the value of the ratio is very sensitive to the value of “before”. If progression is included, the range of that ratio, potentially, goes from 100 to -∞. The index proposed here does range from 0 to 100, around 50% for no change or stable diseases.

Third, the surrogate for volume from the Lugano classification lacks a link to Euclidean geometry since the summation of two-dimensional shape of a certain thickness does not necessarily result in a three-dimensional object of definable volume. To that extend, it does not allow deriving a volume change. In addition, there is no reason to believe that the longest axis is totally in a single slice.

The exception of the marrow metrics compared to the solid tumors does not resolve, or rather reaffirms the question of the need for a bone marrow biopsy [_{m}, SUV_{t} and Liter, should however mitigate the urgency and the fact that responses in solid tumors are associated with higher marrow indexes, but progression and recurrence not. The manual determination of the thresholds (limits) is a weakness [

A Liter | A SUV_{t} | A SUV_{a} | A limit | B Liter | B SUV_{t} | B SUV_{a} | B limit | |
---|---|---|---|---|---|---|---|---|

A Liter | 1 | |||||||

A SUV_{t} | 0.790 | 1 | ||||||

A SUV_{a} | −0.178 | 0.431 | 1 | |||||

A limit | 0.429 | 0.737 | 0.544 | 1 | ||||

B Liter | 0.953 | 0.716 | −0.219 | 0.282 | 1 | |||

B SUV_{t} | 0.855 | 0.870 | 0.119 | 0.494 | 0.831 | 1 | ||

B SUV_{a} | −0.365 | 0.040 | 0.627 | 0.228 | −0.489 | −0.037 | 1 | |

B limit | 0.179 | 0.435 | 0.479 | 0.640 | −0.009 | 0.172 | 0.514 | 1 |

A Liter | A SUV_{t} | A SUV_{a} | A limit | B Liter | B SUV_{t} | B SUV_{a} | B limit | |
---|---|---|---|---|---|---|---|---|

A Liter | 1 | |||||||

A SUV_{t} | 0.808 | 1 | ||||||

A SUV_{a} | −0.479 | 0.059 | 1 | |||||

A limit | −0.021 | 0.349 | 0.520 | 1 | ||||

B Liter | 0.919 | 0.781 | −0.393 | −0.116 | 1 | |||

B SUV_{t} | 0.804 | 0.871 | −0.159 | 0.075 | 0.845 | 1 | ||

B SUV_{a} | −0.435 | −0.308 | 0.359 | 0.087 | −0.448 | −0.279 | 1 | |

B limit | 0.080 | 0.444 | 0.465 | 0.273 | 0.253 | 0.359 | −0.039 | 1 |

the PET tracer, expressed in Standardized Uptake Values, assumes that only three variables matter: The dose injected, the physical half-life of the tracer and injected volume (estimated by weight). It avoids the fact that the renal system excretes and that there is competition between organs and tumors. What automated systems will have to do is to find another criterion than SUV to define organ limits, or base SUV values on the total activity remaining in the body.

Unlike identifying and even diagnosing a lung tumor in a clear pulmonary background, automatic systems have not been very good at identifying organs versus tumors in PET, and even less at identifying the type of tumor [

The method used here would suggest two steps for the automated systems: eliminate normal organs first, then suspect and hunt for tumors.

The method used here would suggest two steps: eliminate normal organs first, then suspect and hunt for tumors. It does not propose a thresholding algorithm to define the limit and volume of tumors. The method proposes, except for the two steps mentioned above, a process essentially based on the fact that the patients, organs, tumors, and the images are three-dimensional, and should be analyzed as such.

The advantage of SUV_{t} and Liter over SUV_{m} is not demonstrated, except that: volume has an important prognostic value, and a total SUV value is more sturdy than a single voxel SUV.

Technically, 3D images require 3D analysis.

The authors declare no conflicts of interest regarding the publication of this paper.

Goris, M.L. and Zhu, H.J. (2020) Metrics to Evaluate PET Response to Therapy Based on 3D Analysis. Open Journal of Medical Imaging, 10, 133-142. https://doi.org/10.4236/ojmi.2020.103013